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Population genomics in species conservation : tools for the study of local adaptation and fisheries management… Kirk, Stephanie Lynn 2009

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   POPULATION GENOMICS IN SPECIES CONSERVATION: TOOLS FOR THE             STUDY OF LOCAL ADAPTATION AND FISHERIES MANAGEMENT IN KOKANEE (ONCORHYNCHUS NERKA)    by   Stephanie Lynn Kirk   B.Sc.H., University of Guelph, 2003     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF   MASTER OF SCIENCE   in   The College of Graduate Studies   (Biology)   THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan)   August 2009   © Stephanie Lynn Kirk, 2009  ii
Abstract   The Okanagan Lake kokanee (Oncorhynchus nerka) population has experienced a dramatic decline in numbers since the late 1960’s, making it the focus of significant conservation efforts.  Despite the decline, two sympatric ecotypes adapted to different reproductive environments continue to exist.  This thesis aims to determine whether divergent selection is driving fine-scale genomic differentiation between the two ecotypes and explore whether the use of adaptive genetic markers for stock identification and population assessment would improve the accuracy and reduce the error associated with conventional kokanee fishery management.  Genomic scans using expressed sequence tag-linked microsatellite markers identified 57 gene regions of adaptive significance, of which seven loci displayed outlier behaviour associated with local adaptation to different spawning environments.  The utility of these outlier loci in genetics-based classifications of Okanagan Lake kokanee stocks is evaluated and the uncertainty surrounding the optimal class and number of genetic markers for differentiating kokanee ecotypes is addressed.  Results from individual assignment and mixture composition analyses show outlier loci consistently perform better than all other classes of markers.  The use of adaptive genetic markers for stock identification and assessment show great promise for improving the accuracy of fundamental abundance estimates and the results suggest a genetics-based approach to stock assessment may be a better alternative to achieving an effective and sustainable kokanee fishery in Okanagan Lake. This thesis presents the first study to directly infer signatures of selection that may be associated with observed variation between Okanagan Lake kokanee ecotypes and presents one of the first applications of population genomics to a natural population of conservation concern.     iii
Table of Contents   Abstract......................................................................................................................................ii Table of Contents .................................................................................................................... iii List of Tables .............................................................................................................................v List of Figures...........................................................................................................................vi Acknowledgements .................................................................................................................vii Dedication .............................................................................................................................. viii Co-Authorship Statement .......................................................................................................ix  1 INTRODUCTION .................................................................................................................1    1.1 Expansion of conservation biology .................................................................................1    1.2 Population genomics ........................................................................................................1          1.2.1 Case study, model organisms....................................................................................3          1.2.2 Case study, non-model organisms ............................................................................4    1.3 Study system .....................................................................................................................6          1.3.1 Systematics ...............................................................................................................6          1.3.2 Reproductive ecotypes..............................................................................................7                   1.3.2.1 Genetic differentiation between ecotypes.....................................................8                   1.3.2.2 Morphology ..................................................................................................8                   1.3.2.3 Life history and behaviour............................................................................9          1.3.3 Population decline...................................................................................................10          1.3.4 Fishery management and conservation initiatives ..................................................11          1.3.5 Study sites and tissue collection .............................................................................12                   1.3.5.1 British Columbia Ministry of the Environment..........................................12                   1.3.5.2 Ecoscape Environmental Consultants Ltd. .................................................13          1.3.6 Genetic marker selection ........................................................................................13    1.4 Thesis objectives.............................................................................................................14    1.5 References.......................................................................................................................15  2 CANDIDATE GENES FOR DIVERGENT SELECTION..............................................19    2.1 Introduction....................................................................................................................19    2.2 Materials and methods ..................................................................................................22          2.2.1 Study site and tissue collection...............................................................................22          2.2.2 Genetic marker selection ........................................................................................24          2.2.3 Molecular characterization .....................................................................................24          2.2.4 Determination of polymorphic loci.........................................................................26          2.2.5 Outlier loci detection ..............................................................................................26          2.2.6 Population genetic analysis.....................................................................................27          2.2.7 Population divergence among ecotypes..................................................................27          2.2.8 Population structure and patterns of outlier behaviour...........................................27    2.3 Results .............................................................................................................................28          2.3.1 Marker characterization of polymorphic loci .........................................................28          2.3.2 Outlier loci detection ..............................................................................................29          2.3.4 Population genetic analysis.....................................................................................30          2.3.5 Neutral and adaptive population divergence ..........................................................32          2.3.6 Population structure and patterns of outlier behaviour...........................................34       iv
2.4 Discussion ..........................................................................................................................36          2.4.1 Efficiency of genome scans ....................................................................................37          2.4.2 Concordance of approaches ....................................................................................38          2.4.3 Management implications for Okanagan Lake kokanee.........................................39          2.4.4 Divergent selection and future directions ...............................................................40    2.5 References.......................................................................................................................42  3 A GENETICS-BASED APPROACH TO STOCK ASSESSMENT ...............................47    3.1 Introduction....................................................................................................................47    3.2 Materials and methods ..................................................................................................51          3.2.1 Origin of genetic data .............................................................................................51          3.2.2 Optimal class of loci for population assignment and stock composition analysis..51          3.2.3 Optimal number of loci for population assignment ................................................54          3.2.4 Mixed population analysis test ...............................................................................54             3.2.5 Effective population size ........................................................................................55    3.3 Results .............................................................................................................................55          3.3.1 Optimal class of loci for population assignment and stock composition analysis..55          3.3.2 Optimal number of loci for population assignment to ecotype ..............................60          3.3.3 Study system context and comparison....................................................................61          3.3.4 Mixed population analysis test ...............................................................................62          3.3.5 Effective population size ........................................................................................63    3.4 Discussion .......................................................................................................................64          3.4.1 Optimal class and number of markers ....................................................................64          3.4.2 Differentiation of ecotypes in Okanagan Lake kokanee.........................................65          3.4.3 Management of the Okanagan Lake kokanee fishery.............................................66          3.4.4 Future directions .....................................................................................................68    3.5 References.......................................................................................................................70  4 CONCLUSIONS ..................................................................................................................73    4.1 Population genomics and conservation........................................................................73          4.1.1 Local implications for Okanagan Lake kokanee ....................................................73    4.2 Significance of thesis research ......................................................................................74    4.3 Limitations of thesis research .......................................................................................75    4.4 Future work....................................................................................................................76    4.5 References.......................................................................................................................78  APPENDICES .........................................................................................................................80 Appendix A Additional data for candidate genes ................................................................80 Appendix B Additional data for a genetics-based approach ............................................101 Appendix C UBC Research ethics board certificate of approval .....................................103   v
List of Tables    Table 1.1 Variation in characteristics of ecotypes in Okanagan Lake, BC ..............................10 Table 2.1 Genetic variation at 57 polymorphic microsatellite loci...........................................31 Table 2.2 Analysis of molecular variance results .....................................................................33 Table 2.3 Pairwise comparisons of FST values .........................................................................33 Table 3.1 Five classes of markers organized from the baseline dataset ...................................52 Table 3.2 Ranking of 57 microsatellite loci..............................................................................56 Table 3.3 Comparison of individual assignment and mixture composition estimates .............58 Table 3.4 Sensitivity results of mixture composition analysis .................................................59 Table 3.5 Comparison of individual assignment success between each spawning locality .....60 Table 3.6 Comparison of results with two previous genetic studies ........................................62 Table 3.7 Results from analysis of unknown individuals.........................................................63 Table A.1 Description of 243 genetic markers screened for use in this study .........................80 Table A.2 Additional description of 57 microsatellite loci ......................................................92 Table A.3 Expanded results of genetic variation at 57 polymorphic microsatellite loci..........95 Table B.1 Second ranking of 57 microsatellite loci................................................................101    vi
List of Figures    Figure 2.1 Locations of kokanee sampling sites on Okanagan Lake........................................23 Figure 2.2 FST vs. heterozygosity values showing outlier loci .................................................29 Figure 2.3 Population structure in Okanagan Lake kokanee ....................................................35 Figure 2.4 Unrooted neighbour-joining trees ...........................................................................36 Figure 3.1 Effect of the number of loci on the percentage of correct individual assignment...61   vii
Acknowledgements    I would like to thank my academic advisor Dr. Michael Russello for providing the opportunity to work on this project and for his guidance over the past two years.  It has been a great pleasure to work with such a dedicated and natural advisor.  In addition, I would like to thank the members of my advisory committee Dr. Robert Lalonde and Dr. Peter Dill who continue to challenge me to think laterally.  I am grateful for their valuable insight and recommendations for my thesis throughout the process.  I would also like to acknowledge my university examiner Dr. Karen Hodges who has been a valued resource and source of inspiration to me for numerous projects along the way.   I have been fortunate to work in an active and enjoyable lab environment full of molecular stars.  I would like to thank my co-founding members of the Ecological and Conservation Genomics Laboratory, Philippe Henry and Anders Gonçalves da Silva for sharing their expertise on population genomics and the never-ending analysis programs that support it.  As well, I am thankful to Danielle Lalonde, Christine Braun and Katy White for their helpful molecular contributions, encouragement and companionship.   I am also thankful to Jenna Benson and Michaela Byrne for their prompt responses to my many fragment analysis request at Fragment Analysis and DNA Sequencing Services.   Fortunately, ample resources supported this research.  I would like to thank Dr. Paul Askey (British Columbia Ministry of Environment), Kyle Hawes and Jason Schleppe (Ecoscape Environmental Consultants Inc Kelowna, BC) and Dr. Eric Taylor (University of British Columbia Vancouver) for their tissue sample contributions that made the genetic work possible.  This research was funded by a Northwest Scientific Association Student Research Grant awarded to myself as well as grants from the Canadian Foundation for Innovation, the Habitat Conservation Trust Fund and the National Science and Engineering Research Council awarded to Michael Russello.  I would especially like to thank my partner and fiancé Jeremy deWaard who has been my greatest source of motivation and happiness through graduate school.  I would not have been able to tackle the distance and deadlines without his generosity and support and I am excited to start the next chapter of our story.   Lastly, I would like to thank my family Diana, Robert and Ron Kirk for their unwavering love and support, which gives me confidence to take these risks, venture to new places and strive for balance amid a busy schedule.    viii
Dedication         To Papa, my earliest source of academic inspiration.   ix
Co-Authorship Statement    Multiple individuals have contributed to Chapter 2 and 3 of this thesis.  Manuscript versions of these chapters will be co-authored when submitted for publication.  My contribution to the identification and design of the research program is shared with co-authors.  However, I have taken lead responsibility for performing the research, data analyses and manuscript preparation.    1
1 INTRODUCTION  1.1 Expansion of conservation biology  A longstanding goal of conservation biology is the preservation of an organism’s ability to adapt to changing environments (Soulé 1980).  In a quest to understand the patterns and processes acting on populations, the field of conservation biology has expanded to incorporate new technologies allowing advanced data collection and interpretation.  Modern conservation biology now incorporates mathematical advances, geographical positioning system technology and genetic techniques in conservation decision making (DeSalle & Amato 2004).  The development of genomic technologies now allows large-scale comparisons of DNA sequences sampled throughout the genome with relative ease (Stinchcombe & Hoekstra 2008).  The use of these genomic technologies in data acquisition and analysis has intensified in the last decade, greatly improving the understanding of genetic parameters which underlie many conservation decisions and has been fundamental in the development of the subdiscipline of population genomics (DeSalle & Amato 2004).     1.2 Population genomics  Population genomics brings together population genetic theory and genomic technologies to simultaneously study genetic variation across a large number of loci within and among individuals and populations (Black et al. 2001, Luikart et al. 2003).  Population genomics provides a better understanding of the role of random genetic drift, mutation and selection in   2
determining genetic variation (Altukhov & Salmenkova 2002, Stinchcombe & Hoekstra 2008).  Knowledge about the genetic basis of adaptation has exploded over the last decade, shedding light on population level variation that allows a population to reach an optimal phenotype (Orr 1998, 2005).  Adaptive variation can be explored by using a variety of molecular techniques common to genomic technology to reveal the genetic origin of phenotypes.  Furthermore, statistical comparison of observed with predicted patterns of phenotypic variation allows us to infer the evolutionary forces shaping genetic variation.  This integrated approach to population genomics studies can uncover the genetic basis of phenotypic variation and the underlying genes responsible for expressing that variation (Black et al. 2001).   There are two main premises that provide the basis for population genomics studies.  The first premise is that population demography and evolutionary history have a similar effect on all neutral loci within the genomes of individuals in a population.  The second premise is that loci under selective pressure display ‘outlier’ behaviour (i.e. significant divergence from the rest of the genome), which will expose patterns of variation within and among populations (Beaumont & Nichols 1996).  The population genomics approach consists of a series of steps as described by Luikart et al. (2003) that begin with the accumulation of tissue samples from many individuals across a wide-range of populations and environmental variables.  These samples are genotyped at many loci spanning across the genome.  Subsequently, statistical tests are used to validate neutral loci and detect loci displaying outlier behaviour.  Outlier loci are removed from the dataset to reliably infer molecular-based estimates of demographic and evolutionary parameters of the population.  Conversely, adaptive information can be gained by correlating patterns of outlier behaviour with population boundaries or environmental variables to infer the causes of outlier loci.       3
1.2.1 Case study, model organisms Our knowledge of genes that influence adaptation is limited (Orr 1998, Rogers et al. 2001), and restricted in most cases to those in model organisms studied under artificial conditions.  One such example is the system of two sympatric, reproductively isolated pea aphids (Acyrthosiphon pisum pisum) that are highly specialized on red clover and alfalfa.  The mechanism and extent of genetic correlations in specialization and speciation between these pea aphids was examined by Hawthorne and Via (2001) through a hybrid cross between two highly specialized, incipient species.  Genetic correlations between resource use and mate choice were found to promote speciation in the pea aphids and it was suggested that sympatric populations that have evolved to use different resources might have a higher probability of speciation if they share a similar genetic architecture.  Similarly, the potential of genome-wide association mapping has been demonstrated in the model plant Arabidopsis thaliana, in which genes known to function in pathogen resistance or flowering time were identified for all of the phenotypes tested (Aranzana et al. 2005).  This plant, which is commonly known as thale cress, is the most widely used model species for studies in plant sciences.   Other examples include the investigation of morphological diversity in East African cichlids, an ideal model system for investigating the genetic basis of vertebrate speciation (Kocher 2004).  Through two generations of hybrid crosses and morphometric analysis of two cichlid species, Albertson and colleagues (2003) were able to estimate one to 11 genetic factors underlying shape variation in oral jaw design associated with opposing modes of feeding.  Similarly, the genetic basis of lateral plate variation present among ancestral marine and derived freshwater three spine sticklebacks has been studied by Barrett and colleagues (2008).   The evaluation of allelic changes in progeny at the candidate gene Ectodysplasin identified positive selection as the mechanism involved in a reduction of lateral plates observed in marine progeny   4
reared in a freshwater environment.  A reduction in lateral plates in a freshwater environment corresponds to a fitness advantage allowing greater individual growth in this model system.  Atlantic salmon (Salmo salar) is also a frequently studied organism in many research areas due to its importance in aquaculture, fish hatcheries and sport fisheries.  Genomic resources, including over 494,000 expressed sequence tags (EST)s are publicly available for Atlantic salmon (GenBank date of inspection 13/07/2009).  Vasemägi and colleagues (2005a) used this extensive EST data to screen for loci under selective pressures in the Atlantic salmon genome.  They described 75 Atlantic salmon EST-linked microsatellite markers and their gene-associated primer pairs as well as the utility of these polymorphic markers in other salmonid species including rainbow trout (Oncorhynchus mykiss).  These studies based on model organisms have laid the groundwork for investigating genome-wide associations between fitness-related traits and segregating variation within natural populations.     1.2.2 Case study, non-model organisms With the rise of large-scale genomic techniques, an increasing number of studies are applying population genomics to natural populations of non-model organisms.  Ongoing research being conducted at Laval University on the lake whitefish (Coregonus clupeaformis) (Rogers et al. 2001, Campbell & Bernatchez 2004) provides excellent examples.  Lake whitefish have sympatrically evolved to adapt to limnetic and benthic ecological niches.  Both studies were aimed at elucidating the genetic basis of population divergence and reproductive isolation in the two whitefish ecotypes using amplified fragment length polymorphism (AFLP) markers to detect polymorphic loci that are under selective pressures.  Rogers and colleagues (2001) used linkage mapping to determine the relative positioning of AFLP markers within the genome as well as calculations of hybrid indices to demonstrate the possibility of associating specific chromosomal   5
regions with identified traits that are undergoing selective pressures in natural populations.  Campbell and Bernatchez (2004) identified restricted gene flow in 14 of 440 AFLP loci that were examined and compared with neutral expectation.  Furthermore, six of these 14 AFLP loci proved to have a higher frequency in one ecotype, which supported the trends of parallel divergence.   Unlike other studies to date, Bonin and colleagues (2006) were the first to apply two outlier detection methods to dominant AFLP data.  They screened a total of 392 AFLP markers for areas of the genome showing unusually high genetic differentiation between populations of the common frog (Rana temporaria) sampled across a selection gradient.  Altitudinal comparisons identified eight candidate loci that played a role in adaptation to different altitudinal environments.  Similarly, outlier loci detection methods were performed in six populations of white spruce (Picea glauca) to investigate local adaptation to various ecological niches.  In this case, 534 single nucleotide polymorphisms were screened throughout the genome to identify genes potentially under selection (Namroud et al. 2008).  Studies such as these are essential in understanding the role of natural selection in shaping biological diversity.  In this thesis, I investigate genes that underlie adaptive variation in Okanagan Lake kokanee (Oncorhynchus nerka).  The unique contribution of this study is that it presents one of the first applications of population genomics to a natural population of conservation concern, as Okanagan Lake kokanee have exhibited drastic declines in recent years.     6
1.3 Study system  1.3.1 Systematics  The Salmonidae are a diverse family consisting of approximately 68 fish species (Koop & Davidson 2008) including such well-known species as Atlantic salmon (Salmo salar), lake whitefish (Coregonus clupeaformis), rainbow trout (Oncorhynchus mykiss) and sockeye salmon (Oncorhynchus nerka).  Sockeye salmon are one of seven species of North American Pacific salmon and are distributed within the Pacific Ocean and Bering Sea from Japan to Siberia and from Alaska to the Columbia River in North America (Burgner 1991).  Kokanee are a form of landlocked sockeye salmon that arose from sea run sockeye salmon (Ricker 1940); they spend their entire lifetime in freshwater, unlike their anadromous conspecifics (Steinhart & Wurtsbaugh 2003).  Populations of kokanee occur from Oregon north through Washington, British Columbia (BC), Yukon, and Alaska (Eschmeyer et al. 1983), yet of particular interest to this thesis is the kokanee population residing in Okanagan Lake, BC, Canada part of the Columbia and Fraser River system (Taylor et al. 1996). Okanagan Lake (49.75, -119.733056) is an oligotrophic, monomictic lake located between the Monashee and Cascade mountain ranges in the south-central interior of BC (Winans et al. 2003).  Okanagan Lake spans 351 km2, has an average depth of 76 m (Taylor et al. 1997) and supports an estimated 22 freshwater fish species, including native kokanee (BC Ministry of Environment Fish Inventory Data Queries 2009; http://a100.gov.bc.ca/pub/fidq/fissSpeciesSelect.do).  Like many other northern temperate freshwater fish, kokanee display noticeable phenotypic divergence and reproductive isolation within sympatric ecotypes.  In Okanagan Lake these ecotypes are expressed as two distinct spawning strategies: shore and stream spawners.   7
1.3.2 Reproductive ecotypes The existence of sympatric shore and stream spawning ecotypes is one of the most significant characteristics of the Okanagan Lake kokanee population.  One ecotype spawns in tributary streams and the other spawns in a beach environment along the shore of lakes (Winans et al. 2003), a reproductive strategy unique among salmonids.  There are notable differences between environmental variables within each of these spawning habitats (Table 1.1).  Female stream spawning kokanee deposit their eggs in a shallow water substrate consisting of rounded gravel (<5 cm in length).  Conversely, female shore spawning kokanee deposit their eggs among large angular rocks (>5 cm in length) in slightly deeper waters (Shepherd 2000). The origin of shore and stream spawning ecotypes with distinct reproductive niches is commonly attributed to a recent postglacial divergence from a common ancestral Oncorhynchus nerka, which inhabited Okanagan Lake following the colonization of the Okanagan watershed and the retreat of the Wisconsinan glaciers <11,000 year ago (Taylor et al. 1997, Wood et al. 2008).  This theory of a recent postglacial divergence is supported by rare mitochondrial DNA haplotypes and allozyme alleles unique to the Okanagan Lake kokanee population (Wood et al. 1994, Winans et al. 1996, Taylor et al. 1996, 1997).  A significant difference in the frequency of mitochondrial DNA haplotypes between reproductive ecotypes suggests that gene flow is restricted and female-mediated between the two reproductive ecotypes, which does not support the idea that the Okanagan Lake kokanee is a single panmictic population (Taylor et al. 1997).  The striking disparity between reproductive phenotypes and the potential for environmentally induced selective pressures raises questions regarding the extent of differentiation between the two reproductive ecotypes of Okanagan Lake kokanee.      8
1.3.2.1 Genetic differentiation between ecotypes Natural selection, gene flow, genetic drift, and mutation interact to shape the patterns of genetic variation in natural populations (Wright 1951, Slatkin 1987).  Patterns of population genetic structure have been examined extensively in salmonids (e.g., Sato et al. 2004, Beacham et al. 2008, Flannery et al. 2007, Narum et al. 2007) including sockeye salmon stocks (Gustafson & Winans 1999, Beacham et al. 2006, Habicht et al. 2007).  In previous studies on the Okanagan Lake kokanee, low levels of genetic differentiation were detected in the frequency of mitochondrial DNA restriction fragment length polymorphism haplotypes (Taylor et al. 1997) and five nuclear microsatellite loci (Taylor et al. 2000) between the two ecotypes.  These findings suggest that the two reproductive strategies are not simply environmentally induced but may be the result of differences at the genetic level, potentially in response to different selective environments during spawning.  Neutral markers have been employed to study such patterns of genetic differentiation in Okanagan Lake kokanee; however, no studies have been conducted to directly infer signatures of selection that may be associated with observed variation between ecotypes.  1.3.2.2 Morphology In addition to detected genetic differentiation between shore and stream spawners, the two ecotypes vary in reproductive phenotypes including morphological, life history and behavioural characteristics.  Male and female shore spawning kokanee are less variable in size and colouration than stream spawners, whereas both sexes of stream spawning kokanee have a larger average body size, display brighter red and green spawning colouration, and the males exhibit more pronounced sexual characteristics such as a hooked jaw and dorsal hump (Dill 1996) (Table 1.1).  The morphological variation visible in spawning kokanee is not diagnostic;   9
however, the two ecotypes may be reliably discriminated only by the number of anal fin rays (Taylor et al. 1997).  No other morphological traits are consistently diagnostic between shore and stream spawners outside the spawning season (Winans et al. 2003).  1.3.2.3 Life history and behaviour In addition to morphology, several life history and behavioural characteristics also vary between the two ecotypes (Table 1.1).  Stream spawner activity peaks during the first week of October, yet is delayed until the fourth week of October in shore spawning individuals (Shepherd 2000, Winans et al. 2003).  Conversely, shore spawning individuals have a faster hatching to emergence time in comparison to their stream spawning counterparts (Taylor et al. 2000).  As well, stream spawning kokanee display pairing and mate defensive behaviour that is not evident in the large schools of shore spawning fish (Dill 1996).  These differences between shore and stream spawning kokanee influence the reproductive potential of both ecotypes and therefore, may be subject to natural selection (Cole 1954).   10
Table 1.1 Variation in characteristics of ecotypes in Okanagan Lake, BC including environmental, morphological, life history and behavioural characteristics of stream and shore spawning kokanee.    Category Characteristic Stream spawning kokanee  Shore spawning kokanee  Reference Spawning location tributaries (e.g., Penticton Cr., Peachland Cr., Mission Cr., Powers Cr.) shoreline areas (e.g., northeast, northwest, central east, southwest) Taylor et al. 1997, 2000 Spawning substrate rounded gravel                      <5 cm in length large, angular rocks            >5 cm in length Shepherd 2000 Environment Spawning depth shallower deeper, 15-100 cm Shepherd 2000 Body size         (nose-fork length) males 26.5 cm mean    (22.9, 35.2 cm range)          23.6 cm mean           (21.6, 25.0 cm range) Shepherd 2000 Colouration bright red and green, not uniform between sexes dark red and green, uniform between sexes Dill 1996 Morphology Sexual characteristics more pronounced (e.g., hooked jaw, dorsal hump) less pronounced Dill 1996 Time of spawning peaks Oct. 1-5 peaks Oct. 25-30 Shepherd 2000 Time of hatching Jan. 1-Apr. 30 Jan. 6-Mar. 15 Shepherd 2000 Life history Time of emergence Apr. 1-June 15 Mar. 15-Apr. 15 Shepherd 2000 Behaviour Spawning behaviour long term pairing, male mate defense daily schooling events Dill 1996   1.3.3 Population decline Okanagan Lake kokanee are currently the focus of significant conservation efforts.  Historical abundance was estimated to exceed 12.5 million fish (Rae 2005) and the population continued to support unrestricted angling into the late 1960’s.  From then on, Okanagan Lake kokanee exhibited a decline, which has been ascribed to several factors.  In 1998, the number of spawning adults of this species of landlocked sockeye salmon was reported at <13,000, an estimated 1% of the numbers observed 35-40 years ago (Shepherd 2000).  With the intention to boost an already thriving population, opossum shrimp (Mysis relicta) were introduced into Okanagan Lake in 1966.  Juvenile kokanee competed with the opossum shrimp for a shared food source, zooplankton, which may have initiated a drastic drop in kokanee population numbers.  In addition to the introduction of an invasive species, the deterioration of spawning habitat from   11
human-induced disturbances, poor lake level management and a decrease in the carrying capacity of Okanagan Lake have also been implicated in the decline of the native kokanee population (Ashley et al. 1998).    1.3.4 Fishery management and conservation initiatives Routine monitoring of spawning adults began in Okanagan Lake in the 1970’s and since then, stock conservation concerns have intensified in fishery management (Shepherd 2000).  Annual abundance estimates of stream and shore spawning kokanee conducted by the BC Ministry of Environment (MOE) have documented a steady decline in Okanagan Lake kokanee until the population reached an all time low in 1998.  These population abundance estimates rely on visual counts of spawning individuals conducted each fall; however, they are complicated by environmental variables as well as behavioural and morphological characteristics.  For example, individuals belonging to the shore ecotype spawn at greater depths and in large numbers while tumbling over each other, making individuals difficult to distinguish.  The darker colouration and smaller body size in shore spawners confounds this problem, even for the experienced eye, and inflates the potential for significant error associated with conventional abundance estimates.  As a result of major conservation concerns surrounding the kokanee population, the Okanagan Lake kokanee fishery was closed and the Okanagan Lake Action Plan was established in 1995 by the BC MOE.  Lake level management was one of the issues identified by the Okanagan Lake Action Plan and addressed by the inclusion of shore spawning life history information in relevant management decisions.  This action has shown an improvement in the number of shore spawning adults in recent years; however, efforts to manage and promote the recovery of the vulnerable Okanagan Lake kokanee population continue today.   12
1.3.5 Study sites and tissue collection Originally, nine geographic locations representing four shore and five stream spawning localities were sampled.  The shore spawning sites included spawning adults sampled from the northeast, northwest, southeast and central west shoreline regions.  Stream spawning adults were sampled from Peachland Creek, Penticton Creek, Mission Creek, Mission Channel and Powers Creek.  Early investigation indicated no evidence for genetic differentiation between individuals originating from Mission Creek and Mission Channel; therefore, these individuals were combined as one sampling unit retaining the name Mission Creek.  Further evaluations that incorporated source spawning locality information were carried out on the resulting eight spawning locations (Figure 2.1).  Approximately 138 tissue and DNA samples were collected from these eight spawning locations for use in this study, constituting a comprehensive geographical coverage of shore and stream spawning localities across Okanagan Lake.  Quantity and sampling techniques are described below for each institution that contributed tissue to this study.  1.3.5.1 British Columbia Ministry of the Environment Individual adult male and female kokanee from both ecotypes were sampled using seine and dip nets by BC MOE and provided by Dr. P. Askey for use in this study (N=128).  Adult kokanee were sampled in the fall of 2007 from each of four primary tributaries of Okanagan Lake (Peachland Creek, Penticton Creek, Mission Creek and Powers Creek; N=71) and each of three shore spawning localities (N=57) located in the northeast, northwest and southeast quadrants of Okanagan Lake respectively.  Operculum punches were taken from individuals at each of the seven sampling localities and stored at UBC Okanagan in 100% ethanol at 4˚C.      13
1.3.5.2 Ecoscape Environmental Consultants Ltd. An additional 10 samples of shore spawning kokanee were collected from new sampling sites located in the central west region of Okanagan Lake (N=10) by Ecoscape Environmental Consultants Ltd., Kelowna, BC in late October of 2007.  After spawning was complete, seining was used to collect individual adult male and female kokanee.  Adipose fin clips were taken from these individuals because removal is minimally invasive and provides good quality and quantity of tissue for DNA analysis (Hammer & Blankenship 2001).  Tissue was stored in 2 mL microcentrifuge tubes at -20°C at UBC Okanagan.   1.3.6 Genetic marker selection   Population genomics studies require sampling many individuals and genotyping many loci (Luikart et al. 2003) using genetic markers, which have the capability of detecting allelic variation at a given locus.  Due to their highly polymorphic nature, high mutation rate and abundance throughout the genome, microsatellites (short tandem repeated sequences) are frequently used to examine genetic differentiation in the genome (Schlötterer 2004).  Microsatellites are mainly located in noncoding regions of the nuclear genome and therefore are presumed to be selectively neutral (Altukhov & Salmenkova 2002).  Expressed sequence tags (EST)s on the other hand are derived from cDNA libraries and thus have the ability to provide insight into transcribed coding regions of an organism’s genome (Nagaraj et al. 2007).   Accordingly, microsatellites that are putatively linked to ESTs offer genetic markers that may be inherited in concert with coding regions of potential adaptive significance.  It is widely accepted that genetic diversity is shaped, in part, by selection in a locus-specific manner (Vitalis et al. 2001).  Population genomics studies are now realizing the advantage of using ESTs, which target coding regions that are more likely to be under selection (Bonin 2008).  In this study, EST-linked   14
microsatellite markers were used, targeting rapidly evolving microsatellites linked to expressed regions of the genome.   1.4 Thesis objectives  The Okanagan Lake kokanee present an ideal system for studying the genetic basis of adaptation in a natural population and exploring the utility of this information in conservation and fisheries management initiatives.  In this thesis, population genomics techniques were used to identify gene regions associated with adaptation to different spawning environments among kokanee in Okanagan Lake and to explore their potential utility for informing fisheries management.  This overall goal was achieved by addressing two separate research objectives in the following two chapters: first, to determine whether divergent selection was driving fine-scale genomic differentiation between the two reproductive ecotypes of Okanagan Lake kokanee.  Second, to explore whether the use of adaptive genetic markers for stock identification and population assessment would improve the accuracy and reduce the error associated with conventional approaches to kokanee fishery management.    15
1.5 References  Albertson RC, Streelman JT, Kocher TD (2003) Directional selection has shaped the oral jaws of Lake Malawi fishes. Proceedings of the National Academy of Sciences of the USA, 100, 5252-5257.  Altukhov YP, Salmenkova EA (2002). DNA polymorphism in population genetics. Russian Journal of Genetics, 38, 989-1008.  Aranzana JM, Kim S, Zhao K, Bakker E, Horton M, Jakob K, Lister D, Molitor J, Shindo C, Tang C, Toomajian C, Traw B, Zheng H, Bergelson J, Dean C, Marjoram P, Nordborg M (2005) Genome-wide association mapping in Arabidopsis identifies previously known flowering time and pathogen resistance genes. PLoS Genetics, 1, 531-539.  Ashley K, Shepherd B, Sebastian D, Thompson L, Matthews S, Vidmanic L, Ward P, Yassien H, McEachern L, Nordin R, Lasenby D, Quirt J, Whall J, Dill P, Taylor E, Pollard S, Wong C, den Dulk J, Scholten G (1998) Okanagan Lake Action Plan Year 1 (1996-1997) and Year 2 (1997-1998) Report. B.C. Ministry of Fisheries Fish Project Report, RD 73.  Barrett RDH, Rogers SM, Schluter D (2008) Natural selection on a major armor gene in threespine stickleback. Science, 322, 255-257.  Beacham TD, Varnavskaya NV, McIntosh B, MacConnachie C (2006) Population structure of sockeye salmon from Russia determined with microsatellite DNA variation. Transactions of the American Fisheries Society, 135, 97-109.   Beacham TD, Varnavskaya NV, Le KD, Wetklo M (2008) Determination of population structure and stock identification of chum salmon (Oncorhynchus keta) in Russia determined with microsatellites. Fishery Bulletin, 106, 245-256.  Beaumont MA, Nichols RA (1996) Evaluating loci for use in the genetic analysis of population structure. Proceedings of the Royal Society of London Series B, 263, 1619-1626.  Black WC, Baer CF, Antolin MR, DeTeau NM (2001) Population genomics: genome-wide sampling of insect populations. Annual Review of Entomology, 46, 441-469.  Bonin A, Taberlet P, Miaud C, Pompanon F (2006) Explorative genome scan to detect candidate loci for adaptation along a gradient of altitude in the common frog (Rana temporaria). Molecular Biology and Evolution, 23, 773-783.  Bonin A (2008) Population genomics: a new generation of genome scans to bridge the gap with functional genomics. Molecular Ecology, 17, 3583-3584.  Burgner RL (1991) Life history of sockeye salmon (Oncorhynchus nerka). In, Pacific Salmon Life Histories (eds Groot C, Margolis L) pp. 1-117. University of British Columbia Press, Vancouver.    16
Campbell D, Bernatchez L (2004) Generic scan using AFLP markers as a means to assess the role of directional selection in the divergence of sympatric whitefish ecotypes. Molecular Biology and Evolution, 21, 945-956.  Cole LC (1954) The population consequences of life history phenomena.  Quarterly Review of Biology, 29, 103-137.  DeSalle R, Amato G (2004) The expansion of conservation genetics. Nature Reviews Genetics, 5, 702-712.  Dill PA (1996) A study of shore-spawning kokanee salmon (Oncorhynchus nerka) at Bertram Creek Park Okanagan Lake, B.C., 1992-1996. B.C. Ministry of Environment, Penticton, B.C. Unpublished report. 23 pp.  Eschmeyer WN, Herald ES, Hammann H (1983) A field guide to Pacific Coast fishes of North America. Houghton Mifflin, Boston, Massachusetts, USA.  Flannery BG, Wenburg JK, Gharrett AJ (2007) Variation of amplified fragment length polymorphisms in Yukon River chum salmon: population structure and application to mixed-stock analysis. Transactions of the American Fisheries Society, 136, 217-231.  Gustafson RG, Winans GA (1999) Distribution and population genetic structure of river- and sea-type sockeye salmon in western North America. Ecology of Freshwater Fish, 8,   181-193.  Habicht C, Seeb LW, Seeb J (2007) Genetic and ecological divergence defines population structure of sockeye salmon populations returning to Bristol Bay, Alaska, and provides a tool for admixture analysis. Transactions of the American Fisheries Society, 136, 82-94.   Hammer SA, Blankenship HL (2001) Cost comparison of marks, tags, and mark-with-tag combinations used in Salmonid research. North American Journal of Aquaculture, 63, 171-178.  Hawthorne DJ, Via S (2001) Genetic linkage of ecological specialization and reproductive isolation in pea aphids. Nature, 412, 904-907.  Kocher TD (2004) Adaptive evolution and explosive speciation: the cichlid fish model. Nature Reviews Genetics, 5, 288-298.  Koop BF, Davidson S (2008) Genomics and the Genome Duplication in Salmonids. K Tsukamoto, T Kawamura, T Takeuchi, TD Beard, Jr. and M J Kaiser, eds. Fisheries for Global Welfare and Environment, 5th World Fisheries Congress, pp. 77-86.  Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2003) The power and promise of population genomics: from genotyping to genome typing. Nature Reviews Genetics, 4, 981-994.      17
Nagaraj SH, Gasser RB, Ranganathan S (2007) A hitchhiker’s guide to expressed sequence tag (EST) analysis. Briefings in Bioinformatics, 8, 6-21.  Namroud MC, Beaulieu J, Juge N, Laroche J, Bousquet J (2008) Scanning the genome for gene SNPs involved in adaptive population differentiation in white spruce. Molecular Ecology, 17, 3599-3613.  Narum SR, Stephenson JJ, Campbell MR (2007) Genetic variation and structure of Chinook salmon life history types in the Snake River. Transactions of the American Fisheries  Society, 136, 1252-1262.  Orr HA (1998) The population genetics of adaptation: the distribution of factors fixed during adaptive evolution. Evolution, 52, 935-949.  Orr HA (2005) The genetic theory of adaptation: a brief history. Nature Reviews Genetics, 6, 119-127.  Rae R (2005) The state of fish and fish habitat in the Okanagan and Similkameen basins.  Report prepared for Canadian Okanagan Basin Technical Working Group, Westbank, B.C.      125 pp.   Ricker WE (1940) On the origin of kokanee: a freshwater type of sockeye salmon. Proceedings of the Royal Society of Canada, 34, 121-135.  Rogers SM, Campbell D, Baird SJE, Danzmann RG, Bernatchez L (2001) Combining the analyses of introgressive hybridization and linkage mapping to investigate the genetic architecture of population divergence in the lake whitefish (Coregonus clupeaformis, Mitchill). Genetica, 111, 25-41.  Sato S, Kojima H, Ando J, Ando H, Wilmot RL, Seeb LW, Efremov V, LeClair L, Buchholz W, Jin DH, Urawa S, Kaeriyama M, Urano A, Abe S (2004) Genetic population structure of chum salmon in the Pacific Rim inferred from mitochondrial DNA sequence variation. Environmental Biology of Fishes, 69, 37-50.  Schlötterer C (2004) The evolution of molecular markers – just a matter of fashion? Nature Reviews Genetics, 5, 63-69.  Shepherd BG (2000) A case history: the kokanee stocks of Okanagan Lake. Pages 609-616 in Proceedings of a conference on the biology and management of species and habitats at risk, editor L.M. Darling. Kamloops, B.C.   Slatkin M (1987) Gene flow in natural populations. Annual Reviews of Ecology and Systematics, 16, 393-430.   Soulé ME (1980) Thresholds for survival: maintaining fitness and evolutionary potential. Pages 151-169 in Conservation biology, An evolutionary-ecological perspective, editor Sinauer, Sunderland, Massachusetts.     18
Steinhart GB, Wurtsbaugh WA (2003) Winter ecology of kokanee (Oncorhynchus nerka): implications for salmon management. Transactions of the American Fisheries Society, 132, 1076-1088.  Stinchcombe JR, Hoekstra HE (2008) Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits. Heredity, 100,  158-170.  Taylor EB, Foote CJ, Wood CC (1996) Molecular genetic evidence for parallel life-history evolution within Pacific salmon (sockeye salmon and kokanee, Oncorhynchus nerka). Evolution, 50, 401-416.  Taylor EB, Harvey S, Pollard S, Volpe S (1997) Postglacial genetic differentiation of reproductive ecotypes of kokanee Oncorhynchus nerka in Okanagan Lake, British Columbia. Molecular Ecology, 6, 503-517.  Taylor EB, Kuiper A, Troffe PM, Hoysak D, Pollard S (2000) Variation in developmental biology and microsatellite DNA in reproductive ecotypes of kokanee, Oncorhynchus nerka: Implications for declining populations in a large British Columbia lake. Conservation Genetics, 1, 231-249.  Vasemägi A, Nilsson J, Primmer CR (2005a) Seventy-five EST-linked Atlantic salmon (Salmo salar L.) microsatellite markers and their cross-amplification in five salmonid species. Molecular Ecology Notes, 5, 282-288.  Vitalis R, Dawson K, Boursot P (2001) Interpretation of variation across marker loci as evidence of selection. Genetics, 158, 1811-1823.  Winans GA, Aebersold PB, Waples RS (1996) Allozyme variability in selected populations of Oncorhynchus nerka with special consideration to populations of Redfish Lake, Idaho. Transactions of the American Fisheries Society, 125, 645-663.  Winans GA, Pollard S, Kuligowski DR (2003) Two reproductive life history types of kokanee, Oncorhynchus nerka, exhibit multivariate morphometric and protein genetic differentiation. Environmental Biology of Fishes, 77, 87-100.  Wood CC, Riddell BE, Rutherford DT, Withler RE (1994) Biochemical genetic survey of sockeye salmon (Oncorhynchus nerka) in Canada. Canadian Journal of Fisheries and Aquatic Sciences, 51, 114-131.  Wood CC, Bickham JW, Nelson RJ, Foote CJ, Patton JC (2008) Recurrent evolution of life history ecotypes in sockeye salmon: implications for conservation and future evolution. Evolutionary Applications, 1, 207-221.  Wright S (1951) The genetical structure of populations. Annals of Eugenics. 15, 323-354.    19
2 CANDIDATE GENES FOR DIVERGENT SELECTION    2.1 Introduction  Variation in life history traits and the influence of natural selection in shaping life history variation within species has long fascinated biologists (Cole 1954).  Patterns in life history traits are shaped by adaptations to favourable conditions and dictate species survival in the face of changing environments (Schaffer & Elson 1975). Knowledge about the genetic basis of adaptation has expanded rapidly over the past decade, suggesting that advantageous mutations foster a gradual approach towards an optimal phenotype in a population (Orr 1998, 2005).  Likewise, recent advancements of genomic technologies and their application to population-level studies continues to improve our ability to explore adaptive variation and to reveal the genetic origin of phenotypes. Population genomics is one field that has emerged from these advancements and provides a better understanding of the role of evolutionary processes influencing the genome by simultaneously exploring genetic variation across a large number of loci within and among individuals and populations (Black et al. 2001, Luikart et al. 2003, Schlötterer 2003, Stinchcombe & Hoekstra 2008).  Our knowledge of genes that influence adaptation is limited (Orr 1998, Rogers et al. 2001), and restricted in most cases to model organisms studied under artificial conditions.  Genomic studies investigating the genetic basis of phenotypic variation have been applied in many model species including the plant Arabidopsis (Aranzana et al. 2005, Mitchell-Olds & Schmitt 2006), threespine sticklebacks (Gasterosteus aculeatus) (Barrett et al. 2008), Atlantic salmon (Salmo salar) (Vasemägi et al. 2005b) and many cichlid species (Mylandia zebra, Tropheops, Rhamphochromis esox, Oreochromis niloticus) (Allender et al.   20
2003).  These studies have laid the groundwork for investigating genome-wide associations between fitness-related traits and segregating variation within natural populations.  With the rise of large-scale genomic techniques, an increasing number of studies are applying population genomics to natural populations of non-model organisms.  One such organism is the North American lake whitefish (Coregonus clupeaformis), which has sympatrically evolved to adapt to limnetic and benthic ecological niches (Fenderson 1964).  Previous studies aimed at elucidating the genetic basis of population divergence and reproductive isolation in the two whitefish ecotypes were able to detect polymorphic loci that are under selective pressures using amplified fragment length polymorphism (AFLP) markers (Rogers et al. 2001, Campbell & Bernatchez 2004).  Similarly, numerous AFLP markers were used to scan the genome of common frogs (Rana temporaria) spread across an altitudinal gradient for signatures of selection.  Candidate loci associated with adaptation to different altitudinal variables were identified using two outlier detection methods and independent comparisons between populations residing at different altitudes (Bonin et al. 2006).  More recently, genomic techniques were also used to identify single nucleotide polymorphisms (SNP)s underlying patterns of variation in six natural populations of white spruce (Picea glauca) (Namroud et al. 2008).  Of the 345 expressed genes analyzed by their SNP frequency differentiation among populations, 5.5% were detected as outliers potentially under selection.  These studies demonstrate the tremendous promise that the application of population genomics holds for exploring adaptive variation in non-model systems.  The Okanagan Lake kokanee present another ideal system for studying the genetic basis of adaptation in a natural population.  One of the most significant characteristics of the Okanagan Lake kokanee population is the existence of two sympatric reproductive ecotypes, one that spawns in tributary streams and the other that spawns along the shore of the lake.  The two   21
reproductive ecotypes differ in morphology, life history and behavioural characteristics (Taylor et al. 1997, 2000).  Stream spawning kokanee have a larger average body size, display brighter red and green spawning colouration and exhibit more pronounced sexual characteristics, whereas shore spawners are less variable in size and colouration (Dill 1996, Taylor et al. 1997, Shepherd 2000).  Stream spawning activity peaks during the first week of October, yet is delayed until the fourth week of October in shore spawners (Shepherd 2000).  Moreover, stream spawners display pairing and mate defensive behaviours, which are not evident in the large schools of shore spawning kokanee (Dill 1996).  At the genetic level, low levels of population differentiation have been detected in the frequency of mitochondrial DNA restriction fragment length polymorphism haplotypes between shore and stream spawning kokanee (Taylor et al. 1997).  This differentiation suggests restricted female-mediated gene flow between the two reproductive ecotypes and counters the idea that the Okanagan Lake kokanee are a single panmictic population.  Likewise, marginally significant levels of genetic differentiation were detected at five nuclear microsatellite loci (Taylor et al. 2000). Collectively, previous research suggests that the two ecotypes are genetically distinct populations diverging within Okanagan Lake, potentially in response to different selective environments during spawning, providing an excellent system to investigate segregating variation and life history evolution.   Okanagan Lake kokanee are presently under threat, as they have experienced a dramatic decline in population numbers since the late 1960’s, making this population the focus of significant conservation efforts.  Several factors have contributed to the decline, including the deterioration of spawning habitat from human-induced disturbances and the decrease in the carrying capacity of Okanagan Lake (Ashley et al. 1998).  However, the introduction of the invasive opossum shrimp (Mysis relicta) in 1966 may assume most of the responsibility for driving the population to an all-time low of an estimated 1% of historic numbers (Shepherd   22
2000).  Despite the dramatic decline, both reproductive ecotypes continue to exist, maintaining morphological, life history and behavioural variation within this population. The present study investigates the genetic basis of adaptation in the Okanagan Lake kokanee by employing a population genomics approach to identify candidate adaptive genes (Luikart et al. 2003).  Genomic scans are performed on numerous loci throughout the genome to detect patterns of DNA polymorphisms.  A variety of statistical tests are used to detect outlier loci, which are loci that deviate from expected neutral genetic variation and represent gene regions exhibiting signatures of selection or regions that are genetically linked to selected loci (Luikart et al. 2003, Bonin et al. 2006).  This study aims to resolve whether divergent selection is driving fine-scale genomic differentiation between the two reproductive ecotypes, and in doing so, presents one of the first applications of population genomics to a natural population of conservation concern.   2.2 Materials and methods  2.2.1 Study site and tissue collection Okanagan Lake is an oligotrophic, monomictic lake located between the Monashee and Cascade mountain ranges in the south-central interior of British Columbia (Winans et al. 2003).  It spans 351 km2, has an average depth of 76 m (Taylor et al. 1997) and supports an estimated 22 freshwater fish species including native kokanee (BC MOE Fish Inventory Data Queries 2009) (http://a100.gov.bc.ca/pub/fidq/fissSpeciesSelect.do), which are the non-anadromous form of sockeye salmon.   Tissue samples were collected during spawning season in the fall of 2007 from each of   23
four primary tributaries of Okanagan Lake (N=71) including Peachland Creek (49.784735,          -119.714618), Penticton Creek (49.493774, -119.579110), Mission Creek (49.877027,                 -119.429492) and Powers Creek (49.833474, -119.644761) and each of four shore spawning localities (N=57) in the northeast (50.034793, -119.446837), northwest (50.068750,                     -119.495884), southeast (49.746542, -119.715971) and central west (49.935635, -119.501467) quadrants of Okanagan Lake.  Operculum punches or adipose fin clips were removed from adult kokanee after spawning was complete and stored in 100% ethanol in 2 mL microcentrifuge tubes at -20 °C.  A total of 138 tissue samples were used in this study spanning a broad geographical coverage across Okanagan Lake (Figure 2.1).     Figure 2.1 Locations of kokanee sampling sites on Okanagan Lake analyzed in this study.  Green triangles denote shore spawning sites including northeast (NE), northwest (NW), southeast (SE) and central west (CW) quadrants and blue squares denote stream spawning sites including Peachland Creek (PAC), Penticton Creek (PNC), Mission Creek (MC) and Powers Creek (POC).  This map is replicated from Google Earth™ mapping service.   24
2.2.2 Genetic marker selection  Population genomics studies are now realizing the advantage of using expressed sequence tag (EST)-based markers, which represent short sequences of transcribed DNA, to identify adaptive candidate loci in natural populations (Bonin 2008, Namroud et al. 2008).  In this study, EST-linked microsatellites were used to target rapidly evolving markers in or near expressed regions of the genome (Nagaraj et al. 2007, Bonin 2008).  Initially, a literature search for EST-linked microsatellite markers described for Salmo or Oncorhynchus species was conducted and selected loci were used for genomic scans (Grimholt et al. 2002, Rexroad et al. 2005, Vasemägi et al. 2005a, Wright et al. 2007).  Subsequently, a comprehensive search of GenBank (Benson et al. 1999) revealed 11,389 ESTs described for Oncorhynchus nerka (Genbank date of inspection 01/08/2008).  These ESTs were scanned for uninterrupted dinucleotide, trinucleotide and tetranucleotide repeats revealing 550 that contained microsatellites.  PCR primers were designed using PRIMER3 software (Rozen & Skaletsky 2000) for 430 amplified fragments of EST-linked microsatellites, which were subsequently crossed referenced with the consortium for Genomics Research on All Salmon (cGRASP) to target those that have known functional annotations (approximately 33%).  Genomic scans performed in this study used 243 genetic markers comprised of 224 EST-linked microsatellites and 19 putatively neutral microsatellites (Table A.1).  2.2.3 Molecular characterization Genomic DNA was extracted from 138 tissue samples (n=128 operculum punches; n=10 adipose fin clips) using the NucleoSpin Tissue kit (Macherey Nagel) following the manufacturer’s suggested protocol.  Each polymerase chain reaction (PCR) contained 14 to 80 ng of DNA template, 1.25 ul of 10X PCR buffer, 1.25 ul of 2 mM dNTP mix, 0.5 ul of 1 mM   25
forward primer, 0.5 ul of 10 mM reverse primer, 0.5 ul of 10 mM M13 fluorescent labeled primer and 0.5 U of taq polymerase in a total volume of 12.5 ul.  KAPAtaq DNA polymerase (KAPA Biosystems) was used for the majority of PCR reactions; however, AmpliTaq Gold DNA polymerase (Applied Biosystems) was used occasionally as a secondary measure to promote PCR amplification.  In order to facilitate automated genotyping, forward primers were 5’-tailed with an M13 sequence [5’-TCCCAGTCACGA-CGT -3’] complementary to the M13 primer 5’-labeled with one of the following fluorescent dyes: 6-FAM (Integrated DNA Technologies), VIC, NED or PET (Applied Biosystems).  This method described by Schuelke (2000) was used to incorporate a fluorescent label into the resulting PCR amplicon.  Likewise, reverse primers were modified following Browstein et al. (1996) for better scoring quality.    First, a touchdown PCR was used with an initial denaturation at 94 ˚C for 2 to 10 min dependent on the manufacturer’s recommendation for the taq DNA polymerase being used.  This denaturation step was followed by 20 cycles at 94 ˚C for 30 sec, 60 ˚C for 30 sec and 72 ˚C for 30 sec with the annealing temperature decreasing by 0.5 ˚C each cycle to 50 ˚C.  The annealing temperature was maintained at 50 ˚C for another 15 cycles followed by a final extension at 72 ˚C for 2 min.   If the initial PCR amplification failed, a second touchdown PCR was conducted with the annealing temperatures lowered by 5 ˚C to a range of 55 to 45 ˚C, which is similar to the PCR optimization strategy used by Vasemägi et al. (2005a).  As a final strategy for amplification, the PCR program was modified to maintain the annealing temperature at 5 ˚C below the lowest melting temperature of the two primers (Innis & Gelfand 1990).  PCR products of four representative samples were electrophoresed in a 1.5% agarose gel and visualized on the gel documentation system Red (Alpha Innotech).  Fragment analysis was performed on an Applied Biosystems 3130XL DNA automated sequencer using the size standard GS500 LIZ to    26
determine fragment length.  Alleles were scored based on their consistent pattern of stutter peaks and peak intensity for individuals at each locus using GENEMAPPER 4.0 (Applied Biosystems).  2.2.4 Determination of polymorphic loci Successfully amplified markers were tested for genetic variability in 16 individuals including two samples originating from each of the eight Okanagan Lake shore and stream spawning localities (Figure 2.1).  Potential loci were identified based on these selection criteria: 1) adhered to patterns of Mendelian inheritance, 2) showed clean peak topography with few or no stutter bands, and 3) adhered to expectations of the documented microsatellite motif.  Loci that were monomorphic or nearly monomorphic in all individuals (i.e. loci with dominant allele frequencies >0.95 in both ecotypes) were excluded to account for allele call and fragment analysis errors (Pompanon et al. 2005).  Once polymorphic markers were identified, the sample size was expanded to a larger screen of 95 individuals consisting of 48 shore spawners and 47 stream spawners to identify candidate genes exhibiting outlier behaviour.   2.2.5 Outlier loci detection  A coalescent-based simulation approach was used to identify outlier loci displaying unusually high and low values of FST by comparing observed FST values with values expected under neutrality (Beaumont & Nichols 1996).  This technique was implemented in LOSITAN Selection Workbench (Beaumont & Nichols 1996, Antao et al. 2008) and based on an island model with migration among populations (Wright 1931), which is suggested to be representative of the Okanagan Lake kokanee population.  Once outliers were detected from the pool of candidate genes, all loci were organized into various groups for further analysis and the exploration of patterns of population differentiation.    27
2.2.6 Population genetic analysis Descriptive statistics were calculated for each ecotype and spawning locality sampled, including the number of alleles (Na) and observed (Ho) and expected (He) heterozygosity at each locus.  To ensure data quality, the existence of technical artifacts, namely null alleles, was evaluated using the software MICRO-CHECKER (Van Oosterhout et al. 2004).  Deviations from Hardy-Weinberg equilibrium were tested by exact tests (Guo & Thompson 1992) using GENEPOP 1.2 (Raymond & Rousset 1995).  With the large amount of genetic markers used in this study, the chance of correlation between loci may be elevated, leading to inaccurate inferences (Stinchcombe & Hoekstra 2008).  Thus, tests for departures from linkage-equilibrium between loci were conducted using GENEPOP 1.2 (Raymond & Rousset 1995).  The sequential Bonferroni correction was implemented in all statistical tests involving multiple pairwise comparisons before evaluating significance (Rice 1989).  2.2.7 Population divergence among ecotypes  The hierarchical organization of genetic variation was assessed using an analysis of molecular variance (AMOVA) (Excoffier et al. 1992) based on FST comparisons within and among reproductive ecotypes as well as on a locus by locus basis.  Likewise, genetic differentiation among each spawning locality was calculated by pairwise FST (Weir & Cockerham 1984), for which 95% confidence intervals were estimated by bootstrapping over loci, all of which were implemented in ARLEQUIN 3.11 (Excoffier et al. 2005).    2.2.8 Population structure and patterns of outlier behaviour A Bayesian approach was used to assess population structure by inferring the number of genetically divergent groups characterized within the dataset and implemented in the software   28
GENELAND 0.7 (Guillot et al. 2005), an extension of the program R 2.9.0 (Ihaka & Gentleman 1996).   Five independent runs were performed under a correlated allele frequency model with 1,000,000 iterations of which every one hundredth was saved and performed without spatial data or a priori knowledge about population divisions. Neighbour joining trees were constructed based on frequency data with Nei’s standard genetic distance measurements (Nei 1987) using the software POPULATIONS 1.2.30 (Langella 1999).  These trees were generated to show the relationships between individuals in each of the shore and stream spawning localities sampled in this study and bootstrap re-sampling was employed as a measure of node support based on 100 and 1000 replicates.  Lastly, the adaptive significance of detected outlier loci was investigated by performing comparative analyses with the wealth of web-based genomic resources available for salmonids, particularly the consortium for Genomics Research on All Salmon (cGRASP; www.cgrasp.org).   2.3 Results  2.3.1 Marker characterization of polymorphic loci PCR amplification was successful for 204 of the 243 EST-linked and putatively neutral microsatellite markers evaluated.  Genotyping results revealed 64.7% of these loci (132/204) appeared to be polymorphic in 16 representative individuals; however, following evaluation of the larger screen of 95 individuals, many did not adhere to the selection criteria.  Subsequently, a total of 57 candidate loci, including nine putatively neutral loci were genotyped for 95 individuals representing eight spawning localities and used for further analysis (Table A.2).  None of the 57 candidate loci had missing data greater than 5% of the 95 individuals.   29
2.3.2 Outlier loci detection  A null distribution was generated using nine putatively neutral microsatellite loci (characterized from 19) described for salmonids, namely One8, One14 (Scribner et al. 1996), One102, One105, One108, One109, One110, One112 (Olsen et al. 1998) and Ssa85 (O’Reilly et al. 1996).  The null distribution provided a baseline for which outlier loci significantly deviating from neutral expectations could be detected.  Using this approach, seven of the 57 (12%) candidate microsatellite markers fell outside of the neutral distribution (Figure 2.2).  Specifically, OMM5007, OMM5091, OMM5125, SK358, SK536, SK626 and SK642 showed significant outlier behavior between the two ecotypes using the coalescent-based simulation approach implemented in LOSITAN (Beaumont & Nichols 1996, Antao et al. 2008).     Figure 2.2 FST versus heterozygosity values showing outlier loci for 48 EST-linked and nine neutral microsatellite loci (each depicted by blue dots) generated by LOSITAN Selection Workbench (Beaumont & Nichols 1996, Antao et al. 2008).  Outlier loci fall outside of the area of neutrality (shown in grey) with a 95% confidence interval.   Various datasets were constructed from the pool of candidate genes for exploring patterns of population differentiation and outlier behaviour.  One dataset comprised all 57 candidate   30
genes evaluated.  A second dataset comprised 50 candidate genes minus the seven outlier loci.  A third dataset contained only the seven outlier loci.  Lastly, a fourth dataset comprised only the nine putatively neutral loci including One14 and Ssa85, which overlapped with the neutral microsatellites used by Taylor and colleagues (2000).   2.3.4 Population genetic analysis Statistical comparisons of expected and observed heterozygosity values showed no significant deviations from Hardy-Weinberg equilibrium at loci within ecotypes or spawning localities (Table 2.1, Table A.3).  In tests for linkage disequilibrium, only five of 1,600 were significant between locus pairwise comparisons of shore spawners, stream spawners and the population as a whole ensuring the loci were independent.   A high degree of variation was detected within this system.  Overall, the mean alleles per locus was 6.74 with a mean expected heterozygosity of 0.52.  For shore-spawners, the number of alleles per locus ranged from 2 to 21 with a mean of 6.67.  Likewise for stream spawning individuals, the number of alleles ranged from 2 to 25 per locus with a mean of 6.81 (Table 2.1).    31
Table 2.1 Genetic variation at 57 polymorphic microsatellite loci (EST-linked and neutral) in two reproductive ecotypes of Okanagan Lake kokanee.  Sample size (N), number of alleles (Na) and expected heterozygosity (He) are shown per locus.      Population   Shore ecotype  Stream ecotype Locus   N Na He   N Na He OMM5003  47 7 0.514  46 7 0.578 OMM5007  48 6 0.559  46 4 0.482 OMM5008  45 7 0.529  47 7 0.529 OMM5032  46 4 0.651  47 5 0.574 OMM5033  46 10 0.816  45 12 0.824 OMM5037  46 7 0.274  47 4 0.267 OMM5053  46 20 0.923  47 25 0.924 OMM5058  47 14 0.854  45 16 0.850 OMM5067  45 3 0.354  46 3 0.481 OMM5075  48 5 0.310  47 6 0.267 OMM5091  48 3 0.061  47 4 0.247 OMM5099  47 5 0.568  46 8 0.572 OMM5108  47 6 0.480  45 7 0.445 OMM5121  47 2 0.489  45 2 0.500 OMM5124  47 3 0.241  47 3 0.271 OMM5125  48 3 0.416  46 3 0.582 CA613  48 5 0.511  47 4 0.510 CA687  46 2 0.364  45 2 0.391 CA983  46 10 0.840  46 15 0.871 SK103  46 4 0.297  45 5 0.338 SK149  48 10 0.757  45 12 0.799 SK170  48 5 0.713  47 5 0.590 SK188  48 11 0.735  47 13 0.789 SK220  48 5 0.278  47 6 0.216 SK249  48 3 0.518  47 3 0.534 SK291  48 2 0.499  47 2 0.482 SK358  48 16 0.879  47 13 0.848 SK365  48 2 0.497  47 2 0.492 SK475  48 4 0.292  46 3 0.332 SK484  48 3 0.221  46 4 0.198 SK536  47 3 0.620  44 3 0.589 SK597  47 5 0.160  47 2 0.173 SK626  48 10 0.656  47 6 0.504 SK634  48 3 0.478  47 3 0.559 SK642  48 6 0.663  46 4 0.684 SK685  48 3 0.239  47 4 0.298 SK691  48 2 0.187  47 2 0.190 SK712  48 2 0.353  47 2 0.390 SK723  48 2 0.489  46 2 0.496 SK740  48 4 0.138  47 3 0.196 SK769  47 2 0.296  47 2 0.268 SK862  48 2 0.264  47 2 0.359   32
  Population   Shore ecotype  Stream ecotype Locus   N Na He   N Na He SK911  48 3 0.100  47 2 0.081 Ots06  48 2 0.021  47 2 0.101 Ots07  48 11 0.728  46 10 0.673 Ots14  48 5 0.511  46 4 0.513 Ots29  47 5 0.568  45 8 0.574 TAP2B  45 2 0.498  46 2 0.491 One102  48 13 0.878  45 13 0.894 One105  48 6 0.532  46 8 0.658 One108  48 21 0.923  45 19 0.916 One109  47 12 0.822  45 13 0.867 One110  48 20 0.909  45 18 0.917 One112  48 17 0.883  45 18 0.891 One14  48 10 0.543  46 8 0.522 One8  48 9 0.493  46 9 0.588 Ssa85  48 13 0.826  47 14 0.844   2.3.5 Neutral and adaptive population divergence In a locus by locus AMOVA, nine of the 57 polymorphic loci showed significant genetic differentiation between the two ecotypes.  The top seven of these nine loci corresponded to the seven outlier loci detected by the coalescent-based simulation approach implemented in LOSITAN (Beaumont & Nichols 1996, Antao et al. 2008).  The two remaining loci that showed significant differentiation in the locus by locus AMOVA but were not detected by the coalescent-based approach fell just within the area of neutrality, namely SK170 and OMM5058 (Figure 2.2).  An AMOVA measuring genetic differentiation based on a comparison of allele frequencies at each locus was conducted using all 57 polymorphic loci including neutral and outlier loci.  It revealed that <1% of the variation occurred among ecotypes while the majority of variation was accounted for within ecotypes (Table 2.2).  The variation among ecotypes dropped even further to <0.1% when AMOVA was calculated using only the nine putatively neutral loci.  Conversely, when the seven outlier loci were evaluated, significant differentiation of 6.3% occurred among ecotypes and 93.7% of variation was accounted for within ecotypes (Table 2.2).    33
Table 2.2 Analysis of molecular variance results based on combinations of EST-linked and neutral microsatellite loci in two reproductive ecotypes of Okanagan Lake kokanee.  Description of    microsatellite loci Variance component Percentage of variation P-value Standard deviation 57 EST-linked and neutral loci Among ecotypes 0.94 0.000* 0.000  Within ecotypes 99.06        Nine neutral loci Among ecotypes 0.07 0.372 0.016  Within ecotypes 99.93        Seven EST-linked outlier loci Among ecotypes 6.31 0.000* 0.000  Within ecotypes 93.69   * P < 0.05  Another statistical test used to investigate the extent of differentiation between the two reproductive ecotypes was a pairwise comparison of genetic differentiation based on FST values.  The seven outlier loci were used to compare each shore spawning and stream spawning location, and 14 of 16 pairwise comparisons were significantly different among ecotype localities (Table 2.3).    Table 2.3 Pairwise comparisons of FST values based on seven EST-linked microsatellite outlier loci in four shore spawning localities (NE-northeast, SE-southeast, NW-northwest, CW-central west quadrants) and four stream spawning localities (PAC-Peachland Creek, PNC-Penticton Creek, MC-Mission Creek, POC-Powers Creek) in two reproductive ecotypes of Okanagan Lake kokanee.     Shore Stream  NE SE NW CW PAC PNC MC POC NE  0        SE  -0.007 0       NW  -0.012 -0.005 0      CW  -0.011 0.008 0.009 0     PAC 0.091* 0.126* 0.086* 0.095* 0    PNC 0.057* 0.088* 0.034* 0.024 0.005 0   MC 0.044* 0.101* 0.046* 0.029 0.081* 0.018 0  POC 0.067* 0.106* 0.060* 0.053* -0.003 -0.015 0.022 0 *P < 0.05    34
2.3.6 Population structure and patterns of outlier behaviour One population cluster was inferred by the dataset in GENELAND 0.7 (Guillot et al. 2005) using only the nine putatively neutral loci (Figure 2.3a) and a correlated allele frequency model without spatial data.   Conversely, two clusters were inferred using the seven detected outlier loci under the same conditions (Figure 2.3b).  Neighbour joining trees constructed based on genotype frequency data also demonstrated the relationship between patterns of outlier loci variation and spawning habitat. The first tree incorporated all 57 polymorphic loci in the dataset including the seven outlier loci and shows the relationships between each of the shore and stream spawning localities sampled in this study (Figure 2.4a).  The second tree shows these relationships in absence of the seven outlier loci (Figure 2.4b).  When the seven informative loci are included in the construction of neighbour joining trees, individuals sampled from distinct clusters corresponding to spawning location (i.e. shore versus stream) group together as most closely related.  Both the inferred number of population clusters and the neighbour joining trees suggest that divergent selection is acting on these outlier loci in relation to spawning behaviour.    35
a.)       b.)  Figure 2.3 Population structure in Okanagan Lake kokanee inferred using GENELAND 0.7 (Guillor et al. 2005) based on multilocus genotype data without prior spatial information.  Results are based on 1,000,000 iterations and 100 thinning for a range of populations (K=1-9).  The number of clusters simulated from the posterior probability are shown for a) nine putatively neutral microsatellite loci (K=1) b) seven outlier loci (K=2).    36
a)       b)  Figure 2.4 Unrooted neighbour-joining trees showing genetic similarities between four shore spawning and four stream spawning localities of Okanagan Lake kokanee.  Trees were based on Nei’s standard genetic distances (Nei 1987) for a.) 57 EST-linked and neutral microsatellite loci (bootstrapped x100) and b.) 50 EST-linked and neutral microsatellite loci once outlier loci were removed (bootstrapped x1000).  Bootstrap values are reported on each corresponding branch.    2.4 Discussion  In the present study, 224 EST-linked and 19 putatively neutral microsatellite markers were screened to detect signatures of adaptive divergence in two ecotypes of Okanagan Lake kokanee sampled from eight spawning locations.  Seven gene regions were identified as candidates of divergent selection, and hence, are potentially involved in the life history differentiation of the two sympatric ecotypes.  Beyond contributing further insight into the efficiency of genome scans and the methodology of outlier detection, these findings have important management implications for Okanagan Lake kokanee and set the stage for further investigations into the role that selection is playing in the life history evolution of kokanee salmon.   37
2.4.1 Efficiency of genome scans This study required considerable effort to uncover a sufficient number of candidate loci that revealed patterns of outlier behaviour and to infer mechanisms of selection acting on the kokanee populations.  Outlier loci detection methods face the general problem of identifying false positives due to statistical bias and/or genotyping errors (Storz 2005).  Therefore, the number of outlier loci detected in this study should be evaluated with caution.  Nonetheless, the rate of outlier detection is noticeably higher than other studies using genomic scans to assess divergence between populations.  The proportion of loci potentially influenced by divergent selection in this study was 12.3% (seven outliers in 57 usable markers), slightly above the average (7.6%; range = 0.9-25%) for the 11 studies reviewed by Holderegger and colleagues (2008: Table 1).  Furthermore, the detection efficiency in the present study was considerably higher than in similar studies investigating differentiation in ecotypes or morphs: 15 outliers of 290 AFLP loci screened between parapatric morphs of the rough periwinkle snail (Wilding et al. 2001); six outliers of 392 AFLP loci in sympatric ecotypes of lake whitefish (Campbell & Bernatchez 2004); and four outliers of 274 AFLP loci screened in two closely related island palms (Savolainen et al. 2006).  The relatively high outlier proportion observed in this study is evidently a function of the marker type (average for microsatellites=14.5%) (Holderegger et al. 2008: Table 1) and their known association with ESTs (6.41% for whitefish, Campbell & Bernatchez 2004; 13.3% for sunflowers, Kane & Rieseberg 2007), supporting the view of Vasemägi et al. (2005c) and Bonin (2008) that genomic scans using EST-linked microsatellite markers provide a superior strategy for detecting functional polymorphisms in natural populations.    38
2.4.2 Concordance of approaches Due to the direct linkage of the EST-linked microsatellite markers with known coding regions, the seven outlier loci identified are strong candidate gene regions associated with adaptive divergence between the two sympatric ecotypes of Okanagan Lake kokanee.  While the nature of outlier detection allows for the possibility of type 1 errors, it is possible to guard against false positives by relying on the comparison of multiple statistical tests for outlier behaviour (Luikart et al. 2003, Bonin et al. 2006).  Separate analyses were performed in this study to verify outlier loci exhibiting signatures of selection and causes of outlier behaviour. First, coalescent and FST-based techniques were used to detect loci that significantly deviated from the remainder of the dataset.  In the two cases where loci deviated significantly in AMOVA but did not using the coalescent-based simulation approach, these two loci were not treated as true candidate loci.  Consequently, the proportion of outlier detection in this study system was 12.3%.  Second, a Bayesian method was used to infer population structure in the genetic dataset with no a priori knowledge about population divisions.  When this method was used to evaluate the most likely number of clusters using the seven outlier loci, two ‘populations’ became evident in the dataset. Loci under selection are known to bias estimates of demography or evolutionary history of populations; therefore, they must be removed from the dataset to reliably infer molecular-based estimates of these parameters (Luikart et al. 2003).  In population genomics studies, it has become commonplace to perform tree building techniques including and excluding outlier loci from the dataset when comparing patterns of outlier behaviour with environmental variables (Wilding et al. 2001, Campbell & Bernatchez 2004, Bonin et al. 2006).  In the third analysis, the neighbour joining tree constructed with outlier loci included in the dataset showed genetic relationships that clearly corresponded to different spawning environments (i.e. the individuals   39
sampled from all four shore spawning localities grouped together as most closely related) with stronger bootstrap support than shown between relationships in the tree constructed in the absence of outlier loci. Overall, strong concordance was found between outlier detection approaches, demonstrating that these outlier loci represent genes or gene regions affected by divergent selection in response to two reproductive strategies.  Since this genetic relationship was lost when outlier loci were excluded from analyses, the possibility exists that there is no long-term genetic differentiation between kokanee ecotypes.  Instead there is detectable gene flow between ecotypes as was demonstrated by Taylor et al. (1997) and these patterns could be explained simply by strong selection acting on these outlier loci.  However, genetic evaluations performed in this study coupled with life history information about the Okanagan Lake kokanee population reaffirm the existence of two ecotypes, likely influenced by divergent selective mechanisms and environmental variables.    2.4.3 Management implications for Okanagan Lake kokanee The discovery of candidate loci underlying significant levels of genetic differentiation between ecotypes provides tools and reiterates the need for unique conservation and management efforts for shore and stream spawning kokanee (Taylor et al. 2000).  Maintaining maximum genetic diversity in the Okanagan Lake kokanee population requires the preservation of sufficient population numbers of both reproductive ecotypes.  In the face of population decline, conservation efforts rely on ecotype abundance estimates to inform strategies for fisheries management.  Current abundance estimates rely on visual counts of shore and stream spawning kokanee during spawning season, a method particularly troublesome for large schools of shore spawning fish and criticized for its potentially high error rates (Taylor et al. 2000).    40
Employing the seven candidate loci shown to underlie adaptation to different spawning environments in population assignment and mixed population analyses has the potential to greatly improve abundance estimates and allow for calculation of estimates outside of the spawning season.  Thus, candidate loci detected in this study have the potential to impact conservation and management practices through application to a genetics-based approach to stock assessment in the Okanagan Lake kokanee (see Chapter 3).  2.4.4 Divergent selection and future directions Evidence supports divergent selection as the mechanism driving fine-scale genomic differentiation in Okanagan Lake kokanee and these seven outlier loci.  However, it is difficult to say with certainty that divergent selection is driving differentiation when the exact location and function of these loci within the genome remains unknown.  The inability to directly link candidate loci with fitness related genotypes proves to be a common limitation of population genomics studies conducted in non-model organisms, as recently reviewed by Bonin (2008) and Stinchcombe & Hoekstra (2008). Combining quantitative trait locus (QTL) mapping, as well as comparisons with the complete genome sequence of a closely related species, would help shed light on the functionality of these candidate loci.  Efforts to generate linkage maps in several salmonid species and sequence the entire genome of the model fish species Salmo salar are currently underway and are projected to be completed in the next few years (Moen et al. 2004, Ng et al. 2005, Wenne et al. 2007).  When this occurs, the location and function of these gene regions may be determined (e.g., metabolic genes, reproductive genes, disease-resistance genes).  With proper annotation, it may be possible to infer biotic or abiotic factors that are driving the physiological, morphological and behavioural divergences observed in both kokanee ecotypes.   41
Investigating signatures of selection acting on these candidate loci at the nucleotide level will also be an important next step to exploring the genetic basis of adaptation (Bonin 2008) in the two reproductive ecotypes of Okanagan Lake kokanee.  Moreover, comparisons with other populations of kokanee where the existence of shore and stream spawning ecotypes has evolved independently (Taylor et al. 1996, 1997) would advance our knowledge of the role these candidate genes play in population divergence.    42
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3 A GENETICS-BASED APPROACH TO STOCK ASSESSMENT   3.1 Introduction  As more knowledge about the genetic complexity of species accumulates, management initiatives are faced with new challenges and continue to search for new approaches to maintain global biodiversity.  One important consideration for management strategies is the suggestion that species with higher variation at the genetic level are better able to respond to fluctuating environmental variables (Hedrick 2001).  For this reason, the evaluation of genotype diversity of individuals and genetic structure of populations is an essential part of management initiatives (Ricker 1981). Particular attention has focused on preserving the genetic diversity of populations that are under threat, for instance by habitat loss and overfishing of weaker stocks.  These factors continue to threaten salmon diversity (Nehlsen et al. 1991) and motivate the integration of genetics in fisheries management initiatives.  Common components, which include the evaluation and implementation of genetic methods used to determine individual assignment (IA) within a population boundary or mixture composition (MC) analysis are shared between natural resource management and conservation, particularly in fisheries management programs (Hallerman 2003, Cadrin & Pastoors 2008).  Also, they include methods for quantifying effective population size (Ne), which is defined as the number of breeding individuals representing the same variance in gene frequency as the whole population (Birkeland & Dayton 2005, Gomez-Uchida & Banks 2006, MacKay 2007).  Similar to other population genetics studies, fisheries management initiatives employing genetic approaches investigate the mechanisms underlying the genetic structuring of species into   48
reproductively isolated populations within their distribution.  This information is of major importance to management initiatives because populations locally adapted to unique niches contribute to the overall genetic diversity of the species and are often characterized by morphological, life history and physiological traits with a genetic basis worthy of conservation efforts (Wenne et al. 2007).  Preserving genetic differentiation within a population may allow it to maintain productivity levels in the face of a changing environment (Hilborn et al. 2003), which is a principal concern to fisheries management.  Although many studies report levels of population genetic diversity, population structure and identify genes under selection, few bridge the gap between acquisition and application of this knowledge to stock assessment and management of populations under threat.  The Okanagan lake kokanee (Oncorhynchus nerka) population provides an ideal system for evaluating the effectiveness of markers of adaptive significance within a genetics-based approach to stock assessment in a wild species of conservation concern. As an economically and ecologically important species, the Okanagan Lake kokanee has been actively monitored by the British Columbia Ministry of Environment (BC MOE) since 1970 (Shepherd 2000).  Resident kokanee salmon are non-anadromous relatives of sea run sockeye salmon and have adapted to exploit two different spawning environments (i.e. tributary streams and lakeshore environments) dividing the population into two sympatric reproductive ecotypes (Wood 1995).  Individuals among ecotypes display marked phenotypic variation in morphological, behavioural and life history traits (Taylor et al. 1997, 2000, Winans et al. 2003), which continue to exist in the face of environmental changes affecting population dynamics.  The introduction of opossum shrimp (Mysis relicta) in 1966 is coincident with a drastic decline in abundance, with kokanee reaching an all time low of <1% of historic numbers in 1998 (Shepherd 2000).  Growing concerns about population health were manifested by the closure of   49
the kokanee fishery in 1995 as well as the inception of a 20-year Okanagan Lake Action Plan.  The increased intensity of management efforts have seen some promising progress, celebrating a substantial milestone with the reintroduction of the Okanagan Lake kokanee fishery in 2005.  Throughout these fisheries management initiatives, the ability to estimate the abundance of individuals in the population is crucial, and as such, annual abundance estimates of shore and stream spawning fish are a major focus of monitoring efforts.  Current enumerations rely on population estimates of spawning kokanee conducted each fall by visually counting the number of individuals.  Important management decisions rely on these visual estimates despite being complicated by many factors including morphology (e.g., darker colouration), reproductive behaviour (e.g., spawning in large schools of fish) and location attributes (e.g., spawning at slightly greater depths) of shore spawning kokanee.  The employment of a genetics-based approach to estimating the abundance of each ecotype has the potential to minimize errors and allow estimates to be calculated at any time of year on a variety of life stages.  A genetic approach would provide flexibility and precision to current management practices, which would undoubtedly benefit this natural population of conservation concern.  Taylor and colleagues (2000) presented the first investigation of applying genetic techniques to the management of Okanagan Lake kokanee.  Mixed population analyses were conducted based on the allelic variation at five putatively neutral microsatellite loci.  These mixed population analyses resulted in high error rates for the assignment of individuals to their respective ecotypes (i.e. 29% and 24% incorrect assignment of shore and stream spawners respectively), suggesting that the class and/or number of genetic markers chosen were inadequate for stock assessment.  In contrast, Withler (2005) found a relatively high success rate for MC (i.e. composition estimates of both shore and stream spawning individuals had an error rate of only 6.2% in a simulation of 50 representatives from each ecotype) using 14   50
microsatellite loci and one MHC locus, which has also been applied to stock assessment of coastal anadromous sockeye salmon (Beacham et al. 2005).  These two studies suggest that marker number may be of critical importance in differentiating the two kokanee ecotypes for IA and MC analyses, as in other population genetics studies (see Bromaghin 2008).  Furthermore, while these two studies used putatively neutral markers, the employment of functionally important markers is increasingly viewed as desirable for successful management of biodiversity (Utter et al. 1993, Vasemägi et al. 2005c, Chapter 2).    To further explore the application of genetic techniques for management of the Okanagan Lake kokanee fishery, the present study expands upon previous work conducted by Taylor et al. (2000) and Withler (2005).  First, using the data generated for seven outlier loci exhibiting signatures of selection (Chapter 2), a thorough comparison of different classes of markers for differentiating the two kokanee ecotypes in both IA and MC analysis was performed.  Second, the uncertainty surrounding the optimum number of loci to be used in such calculations is addressed.  Third, calculation methods of the effective population size of both ecotypes and the total population is demonstrated using neutral microsatellite data, providing estimates for the 2007 breeding season.  And finally, the results in the context of managing the Okanagan Lake fishery is discussed, and recommendations for a genetics-based strategy to aid in conserving and sustaining the diversity of this population is provided.      51
3.2 Materials and methods  3.2.1 Origin of genetic data DNA was isolated from 95 kokanee tissue samples collected during the 2007 spawning season (see Chapter 2).  These samples consisted of 48 individuals from four shore spawning locations (northeast, northwest, southeast and central west quadrants) and 47 individuals from four stream spawning locations (Peachland Creek, Penticton Creek, Mission Creek and Powers Creek) spanning Okanagan Lake.  Genomic scans using 224 EST-linked and 19 putatively neutral microsatellite markers were performed.  Polymerase chain reaction conditions were optimized and fragment analysis was performed to generate multilocus genotype data for 95 individuals at 57 candidate loci (i.e. 48 EST-linked and nine putatively neutral microsatellite loci) that form the baseline dataset used in this study.  Comparing multiple Fst-based outlier approaches, seven EST-linked microsatellite markers were indentified as outlier loci, namely OMM5007, OMM5091, OMM5125, SK358, SK536, SK626, and SK642.  In the present study, for the purpose of mixed population analysis, DNA was isolated from 16 new kokanee tissue samples consisting of two individuals from each of the four shore and four stream spawning locations described above.  Genotype data was generated for these 16 individuals at the seven outlier loci showing signatures of divergent selection in relation to reproductive ecotypes following the protocol outlined in Chapter 2.  3.2.2 Optimal class of loci for population assignment and stock composition analysis A recently developed software program named BELS (backward elimination locus selection, Bromaghin 2008) was designed to prioritize numerous loci in a baseline dataset using an algorithm that evaluates the synergy between loci.  More specifically, it evaluates the ability   52
of all possible locus combinations to resolve multiple populations for the purpose of IA and MC analysis.  The top ranked locus was the locus that caused the greatest reduction in mean performance accuracy when excluded from the baseline dataset (i.e. the collective ability of remaining loci to differentiate between ecotypes).  In this study, 57 loci were evaluated by the mean IA estimation accuracy option implemented in BELS (Bromaghin 2008) and rankings of most effective to least effective were generated for two scenarios: 1) the ability of each locus to assign individuals to their source ecotype (Table 3.2), and 2) the ability of each locus to assign individuals to their source shore or stream spawning location (Table B.1).  The baseline dataset was comprised of 57 loci that fit the following descriptions: seven outlier loci, 50 non-outlier loci, 48 EST-linked and nine non-EST-linked microsatellite loci.  Thus, it was organized into four additional classes of marker types for comparison with the top ranked loci identified as most effective at resolving populations using the algorithm implemented in BELS (Bromaghin 2008) (Table 3.1).  The seven outlier loci detected in Chapter 2 restricted the number of loci used in each category for comparisons of marker types.  Thus, the decision to control for the number of loci at a value of seven in comparisons of different marker types was based on this limiting factor.   Table 3.1 Five classes of markers organized from the baseline dataset and used to compare the performance of individual assignment and mixture composition estimates for selected vs. neutral loci and EST-linked vs. non-EST-linked loci.    Marker comparison Class of markers Total number of loci included in each class of markers  Top 7 BELS ranked loci 57 polymorphic loci 7 outlier loci 7 outlier loci Selected vs.        neutral loci Top 7 non-outlier loci 50 non-outlier loci Top 7 EST-linked loci 48 EST-linked loci EST-linked vs.      non-EST-linked loci Top 7 non-EST-linked loci 9 non-EST-linked loci     53
The ability of the top seven loci in each category of marker type to assign individuals to their mostly likely source ecotype was first assessed by jackknife iterations using a multilocus likelihood algorithm implemented in the program WHICHRUN 4.1 (Banks & Eichert 2000).  Second, genetic classifications were performed based on conditional probabilities of assignment implemented in GENECLASS 2.0 (Piry et al. 2004).  This program incorporated the Bayesian assignment method of Rannala & Mountain (1997) and the probability computation algorithm of Paetkau et al. (2004) for 10,000 simulated re-samplings with a type I error value of 0.05 to predict the percentage of correctly assigned individuals to their source ecotype.  In addition, genetic classifications using only the top five ranked loci were performed under the same parameters to allow for a direct comparison with the five putatively neutral microsatellite loci used by Taylor et al. (2000).  Conversely, in order to assess the ability of the top seven loci to discriminate between source shore and stream spawning localities, the probability computation using Monte-Carlo re-sampling implemented in GENECLASS 2.0 (Piry et al. 2004) was excluded from IA analysis.  Maximum likelihood estimates of stock composition were based on genotype frequency calculations for each locus per ecotype and implemented in the statistical program for analyzing mixtures (SPAM 3.7) (Debevec et al. 2000).   This program was able to accommodate highly variable EST-linked microsatellite data and estimate the point composition of each ecotype found within a kokanee sample.  Input files were generated in the program WHICHRUN 3.2 (Banks & Eichert 2000) for use in the MC analysis of 150 individuals simulated 100 times with replacement at the top seven loci for each class of markers.  In addition, MC analyses were performed for various simulated ratios of shore and stream spawning individuals including 100:0, 90:10, 75:25, and 50:50 in order to test the sensitivity of this method at estimating various proportions of each ecotype in mixed samples.  The top 14 ranked loci were also evaluated for   54
comparison with the 14 putatively neutral microsatellite loci used by Withler (2005) to estimate stock composition of sympatric kokanee ecotypes in Okanagan Lake.  3.2.3 Optimal number of loci for population assignment In addition to the class of molecular markers used in IA analysis, (selected, neutral, EST-linked or non-EST-linked microsatellite markers) there are potential consequences to the number of loci used in assignment estimates of individuals to their source ecotype.  The IA performance of the range of loci available in this study was evaluated for shore and stream spawning individuals as well as averaged over both ecotypes based on the Bayesian assignment method of Rannala & Mountain (1997) and probability computation of Paetkau et al. (2004) for 1,000 simulations with a type I error value of 0.05 as implemented in GENECLASS 2.0 (Piry et al. 2004).  Evaluations were based on the hierarchical ability of loci to assign individuals to their source ecotype ranked by BELS (Bromaghin 2008) and were first performed on the top ranked locus only, subsequently adding the next top ranked locus until 56 loci had been incorporated.   3.2.4 Mixed population analysis test Sixteen individuals of known origin that were not included in the baseline dataset were used to explore the effectiveness of the different datasets for identifying the source ecotype of “unknown” individuals through mixed population analyses.  This approach begins to simulate the products of a mid-water trawl sample of kokanee potentially at various life stages and indistinguishable by phenotypic characters outside of the spawning season.  These “unknown” individuals were scored at the seven outlier loci (see Chapter 2) and compared with the reference baseline dataset of 95 individuals (48 shore and 47 stream spawning kokanee) under the same analysis parameters described above.   55
3.2.5 Effective population size Two point estimation methods for estimating effective population size (Ne) were performed using the nine putatively neutral microsatellite loci for individuals belonging to shore and stream ecotypes, as well as the Okanagan Lake kokanee population overall.  First, the linkage disequilibrium method of Hill (1981) implemented in NeESTIMATOR 1.3 (Peel et al. 2004) was used to calculate Ne based on allele frequency data and a 95% confidence interval.  Second, estimates of Ne were calculated using an alternative linkage disequilibrium method that corrects for the bias associated with small sample sizes (Waples 2006).  This method is implemented in the program LDNE (Waples & Do 2008) and uses a jackknife method to obtain 95% confidence intervals and excluded all alleles with frequencies less than 0.02.   3.3 Results  3.3.1 Optimal class of loci for population assignment and stock composition analysis The seven outlier loci are spread throughout the ranking hierarchy taking the top two positions as well as one of the last positions and falling within the top 44 of 57 overall ranked loci (Table 3.2).  The seven non-outlier and seven EST-linked loci both fall within the top 10 overall ranked loci according to BELS (Bromaghin 2008).  Lastly, the seven non-EST-linked loci fall within the top 32 overall ranked loci and is the only class of markers that does not have a locus that falls within the top three overall ranked loci.    56
Table 3.2 Ranking of 57 microsatellite loci by their synergistic ability to differentiate between ecotypes of Okanagan Lake kokanee using the algorithm implemented in BELS (Bromaghin 2008).  The top seven ranked outlier, non-outlier, EST-linked and non-EST-linked microsatellite loci are identified numerically in each column for use in comparisons of selected vs. neutral and EST-linked vs. non-EST-linked markers.  Marker comparison  Selected vs. neutral loci EST-linked vs. non-EST-linked loci Locus name BELS ranking 7 outlier loci Top 7 non-outlier loci Top 7 EST-linked loci Top 7 non-EST-linked loci OMM5125 1 1  1  SK358 2 2  2  OMM5058 3  1 3  One112 4  2  1 OMM5053 5  3 4  SK188 6  4 5  SK642 7 3  6  Ssa85 8  5  2 One102 9  6  3 CA983 10  7 7  SK626 11 4    SK170 12     OMM5032 13     OMM5003 14     SK536 15 5    One110 16    4 One105 17    5 SK220 18     OMM5091 19 6    SK149 20     One8 21    6 OMM5008 22     SK712 23     SK597 24     Ots06 25     OMM5075 26     OMM5033 27     Ots29 28     CA687 29     SK103 30     SK723 31     One108 32    7 Ots07 33     OMM5099 34     OMM5124 35     SK249 36       57
Marker comparison  Selected vs. neutral loci EST-linked vs. non-EST-linked loci Locus name BELS ranking 7 outlier loci Top 7 non-outlier loci Top 7 EST-linked loci Top 7 non-EST-linked loci OMM5067 37     OMM5037 38     SK740 39     SK769 40     OMM5108 41     SK634 42     SK911 43     OMM5007 44 7    SK484 45     SK862 46     CA613 47     SK365 48     SK475 49     SK685 50     TAP2B 51     OMM5121 52     SK691 53     SK291 54     One109 55     One14 56     Ots14 57           Selected and EST-linked microsatellite loci consistently perform better than neutral and non-EST-linked microsatellite markers for IA and MC analysis in this study system (Table 3.3).  When the seven outlier microsatellite loci were included in the IA analysis, means of 79.0% and 82.1% of the 95 individuals were correctly assigned to their source ecotype using WHICHRUN 4.1 (Banks & Eichert 2000) and GENECLASS 2 (Piry et al. 2004) respectively.  When the seven outlier microsatellite loci were included in the MC analysis using SPAM 3.7 (Debevec et al. 2000), the composition of shore and stream spawners were estimated with a mean of 100% accuracy for the 95 individuals in the baseline population.  Conversely, when the non-outlier or non-EST-linked microsatellite loci were used to evaluate IA and MC, the ability to identify the   58
appropriate source ecotype decreased on average from 9.5% between the seven outlier and top seven non-outlier loci to as much as 46.4% between the seven outlier and top seven non-outlier loci (Table 3.3).  Tests of the sensitivity of MC analysis demonstrated its ability to accurately estimate ecotype proportions for various simulated ratios of shore and stream spawners in a mixed sample with estimates deviating from true values by no more than 0.7% (Table 3.4). The top seven overall BELS-ranked loci had the highest mean success for the IA methods evaluated using WHICHRUN 4.1 (Banks & Eichert 2000).  However, did not perform as well as the seven outlier or top seven EST-linked microsatellite loci when using the algorithms implemented in GENECLASS 2 (Piry et al. 2004) and SPAM 3.7 (Debevec et al. 2000) for IA and MC analysis respectively.   Table 3.3 Comparison of individual assignment and mixture composition estimates generated by three different analysis programs for five classes of markers including the top seven ranked loci based on BELS (Bromaghin 2008) as well as the top seven outlier, non-outlier, EST-linked and non-EST-linked microsatellite loci.  Results are expressed as percentage of correct individual assignment (IA) for 95 individuals and percentage of mixture composition (MC) estimates with standard deviations for simulations of 50 shore and 50 stream spawning fish as well as the overall mean success of IA and MC reported for each class of markers (in bold).     Marker comparison   Selected vs. neutral loci EST-linked vs. non-EST-linked loci  Source ecotype (% success) Top 7 BELS ranked loci 7 outlier loci Top 7 non-outlier loci Top 7 EST-linked loci Top 7 non-EST-linked loci WHICHRUN 4.1      Shore 91.7 75.0 72.9 81.3 52.1 Stream 85.1 83.0 66.0 78.7 57.4 Mean success 88.4 79.0 69.5 80.0 54.7 GENECLASS 2      Shore 72.9 81.3 52.1 68.8 33.3 Stream 83.0 83.0 80.9 89.4 85.1 IA Mean success 77.9 82.1 66.3 79.0 58.9 SPAM 3.7      Estimation of 50 shore spawners 46.1 (4.2) 49.5 (4.8) 33.1 (4.6) 47.4 (4.5) 42.1 (4.6) Estimation of 50 stream spawners 46.0 (4.0) 50.5 (4.8) 20.5 (3.6) 46.9 (4.6) 36.7 (4.7) MC Mean success 92.1 100.0 53.6 94.3 78.8   59
Table 3.4 Sensitivity results of mixture composition analysis based on the seven outlier loci.  True mixture composition values are listed for comparison with the estimated mixture composition expressed as a percentage with standard deviations (SD) of various ratios of 150 shore and stream spawning fish in seven simulations of mixed samples.     Ecotype True composition (%) Estimated composition (% ± SD) Shore spawners 100 99.8 (0.37) Simulation 1 Stream spawners  0 0.2 (0.37) Shore spawners  90 89.4 (3.5) Simulation 2 Stream spawners  10 10.6 (3.5) Shore spawners  75 75.1 (4.0) Simulation 3 Stream spawners  25 24.9 (4.0) Shore spawners  50 49.3 (4.3) Simulation 4 Stream spawners  50 50.7 (4.3) Shore spawners  25 24.4 (4.3) Simulation 5 Stream spawners  75 75.6 (4.3) Shore spawners  10 10.3 (2.9) Simulation 6 Stream spawners  90 89.7 (2.9) Shore spawners  0 0.1 (0.26) Simulation 7 Stream spawners  100 99.9 (0.26)   Similar to the results demonstrating the ability of different classes of markers to identify the correct source ecotype of Okanagan Lake kokanee, the seven outlier loci were best able to differentiate between the eight source spawning localities sampled in this study (Table 3.5).  Mission Creek samples showed the highest assignment success in all types of markers with the exception of non-outlier loci.  Overall, the total mean success of IA was 20.0% for the seven outlier loci and decreased to 18.9%, 15.8%, 14.7% and 12.6% for evaluations using the top seven ranked, EST-linked, non-EST-linked and non-outlier microsatellite loci respectively.    60
Table 3.5 Comparison of individual assignment success between each spawning locality including shore (n=4) and stream (n=4) for five classes of markers namely the top seven ranked loci implemented in BELS (Bromaghin 2008) (Table B.1) as well as the top seven outlier, non-outlier, EST-linked and non-EST-linked microsatellite loci.  Individual assignment (IA) is expressed as a percentage of correct assignment to source shore or stream spawning locality.   Marker comparison   Selected vs. neutral loci EST-linked vs. non-EST-linked loci Source spawning location for IA  Sample size (n) Top 7 BELS ranked loci 7 outlier loci Top 7 non-outlier loci Top 7 EST-linked loci Top 7 non-EST-linked loci northeast shore 14 14.3 21.4 0.0 0.0 14.3 southeast shore 14 21.4 0.0 7.1 14.3 21.4 northwest shore 14 14.3 7.1 7.1 21.4 0.0 central west shore 6 16.7 16.7 0.0 0.0 16.7 Peachland Creek 10 10.0 50.0 30.0 10.0 10.0 Penticton Creek 10 20.0 20.0 30.0 0.0 20.0 Mission Creek 18 33.3 33.3 11.1 44.4 22.2 Powers Creek 9 11.1 11.1 22.2 11.1 11.1 Mean success (%) -  18.9 20.0 12.6 15.8 14.7    3.3.2 Optimal number of loci for population assignment to ecotype  Various numbers of loci were included in the evaluation of population assignment success implemented in GENECLASS 2 (Piry et al. 2004) (Figure 3.1).  When only the single top ranked locus was used for IA, 77.9% of individuals overall were correctly assigned to their source ecotype. Likewise, when the top four ranked loci were used for IA of kokanee to their source ecotype, 80.0% shore and stream spawning individuals were correctly assigned, showing the highest mean success for this analysis.   As additional loci were added to the evaluation, correct IA of stream spawners increased, peaking at 93.6% with 21 loci.  However, IA of individuals originating from the shore spawning ecotype decreased to a minimum of 47.9% with 56 loci.     61
 Figure 3.1 Effect of the number of loci on the percentage of correct individual assignment to shore (n=48) and stream (n=47) spawning ecotypes independently, as well as the mean percentage of correct IA to source ecotype of the total population of Okanagan Lake kokanee (N=95).  Estimates of IA are calculated using the top seven ranked loci able to differentiate ecotypes according to BELS analysis (Bromaghin 2008).  The first seven data points are emphasized by a log scale first representing the single top ranked locus followed by increments of one additional loci up to seven.  The next seven successive data points represent seven additional loci each point to a total of 56 loci.     3.3.3 Study system context and comparison  For comparison of IA and MC estimates generated for the Okanagan Lake kokanee population with previous studies, the top five and 14 overall ranked loci were used to replicate the number of loci and IA parameters used by Taylor et al. (2000) implemented in GENECLASS 2 (Piry et al. 2004) as well as the number of loci and MC parameters used by Withler (2005) implemented in SPAM 3.7 (Debevec et al. 2000).  There was limited overlap of identical markers used in these comparisons, however, markers Ssa85 and One14 were shared between datasets with Taylor and colleagues (2000) and marker One 8 was shared between datasets with Withler (2005).  Using the top ranked loci according to BELS analysis (Bromaghin 2008), IA and MC estimates showed little improvement in accuracy (Table 3.6).        62
Table 3.6 Comparison of results with two previous genetic studies on the Okanagan Lake kokanee population (Taylor et al. 2000, Withler 2005).  Previous results are compared with the individual assignment (IA) and mixture composition (MC) analysis success (%) of the top five and 14 ranked loci able to differentiate shore and stream spawning individuals in this dataset (see Table 3.2).  Differences in the size of the baseline and spawning locations sampled for each study are also given (SP-Squally Point, OC-Okanagan Centre, PT-Paul's Tomb, MC94-Mission Creek 1994, MC97-Mission Creek 1997, PAC94-Peachland Creek 1994, PAC97-Peachland Creek 1997, POC-Powers Creek, NE-northeast shore, NW-northwest shore, SE-southeast shore, CW-central west shore, MC-Mission Creek, PAC-Peachland Creek, PNC-Penticton Creek, BP-Bertram Creek Park and RI-Rattlesnake Island).      5 neutral msat loci* (Taylor et al. 2000) Top 5 BELS ranked loci 14 neutral msat loci** (Withler 2005) Top 14 BELS ranked loci IA to shore (% success) 71.0 75.0 - - IA to stream (% success) 76.0 76.6 - - Estimation of 50        shore spawners (%) - - 53.1 (3.9) 49.6 (4.4) Estimation of 50      stream spawners (%) - - 46.9 (3.9) 50.4 (4.4) Size of baseline (N) 228 95 314 - 491 95 Shore locations SP, OC, PT NE, NW, SE, CW BP, RI NE, NW,  SE, CW Stream locations MC94, MC97, PAC94, PAC97, POC MC, PAC, POC, PNC MC, PAC, POC MC, PAC, POC, PNC *Omy77, Ssa85, Ots3, One14, Ots103 (Taylor et al. 2000)   **Ots2, Ots3, Ots100, Ots103, Ots107, Ots108, Oki1a, Oki1b, Oki6, Oki10, Oki16, Oki29, One8, Omy77 (Withler 2005)    3.3.4 Mixed population analysis test  All 16 new individuals representing samples of unknown ecotype origin were incorporated in IA evaluations implemented in WHICHRUN 4.1 (Banks & Eichert 2000) and GENECLASS 2 (Piry et al. 2004).  In WHICHRUN 4.1 (Banks & Eichert 2000), 50% of shore spawners and 87.5% of stream spawners were correctly assigned to their source ecotype (Table 3.7).  Conversely, results from GENECLASS 2 (Piry et al. 2004) showed higher success at assigning individuals originating from the shore ecotype (87.5%) than the stream ecotype (37.5%).  Estimates of MC based on the 16 unknown individuals were able to incorporate only five and four shore and stream spawning individuals respectively.  The genetic profiles of the   63
missing individuals did not have enough overlap with the genetic profiles included in the baseline dataset, thus, they could not be included in computations implemented in SPAM 3.7 (Debevec et al. 2000).  The estimated composition of each ecotype incorporating nine samples was 67% and 33% respectively, which deviated from the true 55.5% and 44.5% split of individuals originating from each ecotype.   Table 3.7 Results from analysis of unknown individuals.  Individual assignment (IA) of 16 unknown individuals and mixture composition (MC) analysis of nine unknown individuals scored at seven outlier loci namely OMM5007, OMM5091, OMM5125, SK358, SK536, SK626 and SK642.  A total of 95 known individuals (48 shore and 47 stream spawning kokanee) were scored at the same seven outlier loci to form the baseline dataset.  Results from three analysis programs are reported as well as the number of individuals included in each analysis.      Shore source ecotype (% success) Stream source ecotype (% success) WHICHRUN 4.1 50.0 87.5 Number of individuals 8 8 GENECLASS 2 87.5 37.5 IA Number of individuals 8 8 SPAM 3.7 66.6 33.3 MC Number of individuals 5 4   3.3.5 Effective population size Effective population size was calculated using nine putatively neutral microsatellite loci and resulted in the following estimates of Ne performed using NeESTIMATOR 1.3 (Peel et al. 2004) (95% confidence intervals are given in parentheses): 1039.0 (309.7, infinity) for shore, 243.3 (149.8, 597.4) for stream and a total of 1859.9 (654.3, infinity) for the overall population.  Estimates of Ne were calculated by LDNE (Waples & Do 2008) for comparison and resulted in values of 1130.3 (281.5, infinity) for shore, 431.5 (140.4, infinity) for stream and a total of 1251.7 (324.6, infinity) for the overall population.    64
3.4 Discussion  Individual assignment and MC analysis methods were used to estimate the most probable source ecotype and the proportion of each ecotype present in mixed samples of Okanagan Lake kokanee.  Outlier, neutral, EST-linked and non-EST-linked microsatellite markers were used in both methods and direct comparisons were made with the success rates of past studies.  The results obtained from this study have applications for determining the optimal choice of marker class and number of loci to be used in genetics-based approaches to fisheries management.  3.4.1 Optimal class and number of markers The power of outlier loci to identify source ecotypes and differentiate between populations was superior to all other classes of marker used in this study.  The 14 microsatellite markers used in Withler (2005) also performed well; however, it is difficult to compare their findings with the performance of outlier loci due to the inherent differences in study design (Table 3.6).  Mixture composition analysis was based on samples collected from different shore and stream spawning localities in Withler (2005) and low levels of genetic differentiation among source spawning locations will likely bias a direct comparison (R. Withler, personal communication).  Also, evaluations of the 14 microsatellite markers are supported by a baseline dataset 3-5 times larger than the baseline dataset used in this study.  Nevertheless, outlier loci consistently outperformed other classes of loci evaluated in this study suggesting the use of outlier loci take precedence over all other classes of markers when performing IA and MC analyses.   When datasets are not screened for outlier loci, ranking methods such as BELS (Bromaghin 2008) can provide an effective secondary method for selecting loci for employment in IA and MC analyses.   65
Increasing the number of loci used in IA and MC analysis is supported by a natural expectation that the precision and accuracy of these estimates will increase as well.  However, observations by Scribner et al. (1998) and Winans et al. (2004) did not match these expectations.  When subsets of loci were used in MC analysis and compared with estimates based on their complete datasets, there was little reduction in precision or accuracy.  In this study, evaluations of the optimum number of loci to be used for IA analysis support these findings by suggesting that as few as four loci could be used to most accurately assign individuals to their source ecotype.  Conversely, mean estimates deteriorated when incorporating greater than 14 loci showing disparity between the ability of informative loci to differentiate between kokanee ecotypes.    3.4.2 Differentiation of ecotypes in Okanagan Lake kokanee The ability of the top seven ranked loci characterized in this study system to identify ecotype origins diverged dramatically between shore and stream spawners. The discrepancy between ecotypes could be explained by three hypotheses: first, computational issues resulting from inaccurate knowledge of allele proportions was amplified when many loci are used (Guinand et al. 2004).  Second, in light of evolutionary time, shore spawners are most likely derived from stream spawners; therefore there may be a stronger diagnostic genetic signal in stream spawners less evident in shore spawners.  Lastly, the delay in shore spawner activity, which peaks two weeks later than stream spawners (Shepherd 2000) may allow stream spawners to opportunistically spawn with shore spawners if they are too late to pair with another stream spawner (Lin et al. 2008). All three hypotheses would reduce genetic recognition of shore spawning kokanee revealed by diverging IA success among shore and stream spawners with the inclusion of more genetic markers.   66
Is it possible to differentiate shore and stream spawning individuals with perfect precision?  There is evidence from MC analyses performed in this study and implemented in SPAM 3.7 (Debevec et al. 2000) that 100% accurate recognition of ecotypes is possible, however a definitive answer to this question is unattainable until IA and MC estimates are evaluated temporally and the baseline dataset is expanded.  A greater baseline dataset and an increase in the number of unknown individuals included in the mixture dataset would address the current limitations of mixed population analyses by providing a comprehensive suite of reference genetic profiles representative of the total population and allowing a more comprehensive evaluation of unknown individuals.  When compared with previous attempts to implement a genetics-based approach to Okanagan Lake kokanee fishery management, this study suggests that outlier loci perform better than non-outlier loci and that incorporating seven loci in IA and MC analyses may perform better than five and 14 putatively neural loci used in Taylor et al. (2000) and Withler (2005) respectively.  This comparison with previous genetics-based approaches resembles the comparison of ‘apples to oranges’ because fundamental differences in the size of the baseline and the shore and stream spawning localities sampled are evident (Table 3.6).  Similar conditions must be met in these two categories before a true comparison of the previous classes and numbers of loci can be performed and recommendations for the optimal class and number of loci can be made for the Okanagan Lake kokanee population.  3.4.3 Management of the Okanagan Lake kokanee fishery    This study demonstrates how a genetics-based approach to kokanee fishery management can be used to improve the accuracy and decrease the error associated with conventional methods of estimating abundance of individuals.  To this end, it is imperative to perform MC analysis to estimate the proportion and discriminate between the two sympatric ecotypes of   67
Okanagan Lake kokanee outside of the spawning season and potentially at different life stages.  The benefits of this analysis to fisheries management initiatives will be fourfold.  First, it will open the door to more effective methods of sampling by using tissue samples collected by trawling or from creel surveys collected for an entire sampling year.  Second, it will permit the estimation of angler impact on each ecotype, enabling the BC MOE to adjust catch limits for conservation purposes.  Third, it will permit the estimation of the relative population size of each ecotype in order to assign limited funding in the most effective way.  Last, it will allow estimation of Ne for both ecotypes using the proportion of each provided by MC analysis.   With this information there are two approaches to genetics-based abundance estimates that can be implemented using the techniques evaluated in this study.  A genetics-based approach can be used to calculate Ne for the total population and multiplied by the values of the MC ratio of shore and stream spawners.  Conversely, IA can be performed on a sample of unknown kokanee and Ne can be calculated separately for the estimated proportion of each ecotype determined by the IA analysis.  This latter approach will become more feasible once the current limitations to IA are addressed by expansion of the baseline dataset.  In the interim, there are a series of steps that can be followed to calculate Ne for the purpose of fisheries management.  Step one involves building up the baseline of known samples, particularly at each spawning location.  The second step in a genetics-based approach to kokanee fishery management involves sampling unknowns and performing MC analysis using the suite of optimal markers to determine the proportions of shore and stream spawning fish as implemented in the software SPAM 3.7 (Debevec et al. 2000).  The last step is to employ neutral markers for Ne estimates of the total population and each ecotype independently.  In order to make the values of Ne more meaningful, a comprehensive genetics-based approach to kokanee fishery management must continue in parallel with BC MOE’s current   68
methods to estimate census population size. In a comprehensive study by Frankham (1995) comparing Ne/Nc estimates for 102 wildlife species, effective population size was reported to be on average 10% of the census population size in wild populations (Frankham 1995, MacKay 2007).  However, it may in fact be much higher in fish species, as demonstrated via microsatellite analysis in the New Zealand snapper population (Hauser et al. 2002).  This study was restricted to calculations of effective population size at one point in time from one sampling event.  However, there are many temporal methods that compare changes of allele frequency from two or more sampling events over time (e.g., samples from one spawning season to the next).  Over several years, an accurate idea of the Ne/Nc ratio in the Okanagan Lake kokanee population will be determined by the coupled approach of genetics-based and conventional methods.  Also as more samples are processed going forward, temporal methods using moments based F-statistics for calculating Ne  (Krimbas & Tsakas 1971, Nei & Tajima 1981, Pollock 1983, Waples 1989) can be incorporated in long-term management initiatives of the Okanagan Lake kokanee.  3.4.4 Future directions  It is most critical to determine the optimal number of loci, which can be defined and used as a standard protocol for genetic studies involving the Okanagan Lake kokanee as well as studies of similar systems, much like the 14 microsatellite loci used to differentiate between stocks of coastal Pacific sockeye salmon (Beacham et al. 2005) and applied to this population (Withler 2005).  In light of this study, outlier loci and EST-linked microsatellite loci perform better than other classes of markers in direct comparisons.  It needs to be determined if 100% differentiation of ecotypes is possible, or even necessary for an effective genetics-based approach to kokanee fishery management.  In doing so, a suite of outlier loci and neutral loci   69
able to achieve all components of a fishery management plan will be determined.  As the next step, increasing the baseline to at least 100 fish sampled from equivalent spawning locations is recommended, as first suggested by Withler (2005), which will allow for comparisons of class and number of loci under equal conditions as well as show how close IA and MC estimates can come to 100% accuracy.  Subsequently, the suite of optimal loci can be applied to genetics-based studies of other populations of management interest (e.g., Wood Lake).   The use of adaptive genetic markers for stock identification and assessment shows great promise for enhancing the percentage of correct assignment of kokanee to their populations of origin (i.e. shore and stream spawning ecotypes).  Implementation of this approach to stock assessment will benefit the local fish population through application to stock assessment, long-term ecological monitoring and the evaluation of current fisheries management practices.  Thus, a genetics-based approach to stock assessment may be the better alternative to non-genetics-based approaches for achieving an effective and sustainable kokanee fishery in Okanagan Lake.    70
3.5 References    Banks MA, Eichert W (2000) Whichrun (version 3.2): a computer program for population assignment of individuals based on multilocus genotype data. Journal of Heredity, 91, 87-89.  Beacham, TD, Candy JR, McIntosh B, MacConnachie C, Tabata A, Kaukinen K, Deng L, Miller KM, Withler RE, Varnavskaya NV (2005) Estimation of stock composition and individual identification of sockeye salmon on a Pacific Rim basis using microsatellite and major histocompatibility complex variation. Transactions of the American Fisheries Society, 134, 1124-1146.  Birkeland C, Dayton PK (2005) The importance in fishery management of leaving the big ones. Trends in Ecology and Evolution, 20, 356-358.  Bromaghin JF (2008) BELS: backward elimination locus selection for studies of mixture composition or individual assignment. Molecular Ecology Resources, 8, 568–571.  Cadrin SX, Pastoors MA (2008) Precautionary harvest policies and the uncertainty paradox. Fisheries Research, 94, 367-372.  Debevec EM, Gates RB, Masuda M, Pella J, Reynolds JM, Seeb LW (2000) SPAM (Version 3.2): statistics program for analyzing mixtures. Journal of Heredity, 91, 509-510.  Frankham R (1995) Effective population size/adult population size ratios in wildlife: a review. Genetic Research, 66, 95-107.  Gomez-Uchida D, Banks MA (2006) Estimation of effective population size for the long-lived darkblotched rockfish Sebastes crameri. Journal of Heredity, 97, 603-606.  Guinand B, Scribner KT, Topchy A, Page KS, Punch W, Burnham-Curtis MK (2004) Sampling issues affecting accuracy of likelihood-based classification using genetical data. Environmental Biology of Fishes, 69, 245-259.  Hallerman EM, ed. (2003) Population Genetics, Principles and Applications for Fisheries Scientists. American Fisheries Society, Bethesda, Maryland.   Hauser L, Adcock GJ, Smith PJ, Ramirez JHB, Carvalho GR (2002) Loss of microsatellite diversity and low effective population size in an overexploited population of New Zealand snapper (Pagrus auratus). Proceedings of the National Academy of Sciences, USA, 99, 11742-11747.  Hedrick  PW (2001) Conservation genetics, where are we now?  Trends in Ecology & Evolution, 16, 629-636.  Hilborn R, Quinn TP, Schindler DE, Rogers DE (2003) Biocomplexity and fisheries sustainability. Proceeding of the National Academy of Sciences, 100, 6564-6568.   71
Hill WG (1981) Estimation of effective population size from data on linkage disequilibrium Genetic Research, 38, 209-216.  Krimbas CB, Tsakas S (1971) The genetics of Dacus oleae V. Changes of esterase polymorphism in natural population following insecticide control-selection or drift? Evolution, 25, 454-460  Lin J, Quinn TP, Hilborn R, Hauser L (2008) Fine-scale differentiation between sockeye salmon ecotypes and the effect of phenotype on straying. Heredity, 101, 341-350.  MacKay T (2007) Wild populations are smaller than we think: a commentary on ‘Effective  population size/adult population size ratios in wildlife: a review’ by Richard Frankham.  Genetic Research, 89, 489.  Nehlsen W, Williams JE, Lichatowich JA (1991) Pacific salmon at the crossroads: stocks at risk from California, Oregon, Idaho, and Washington. Fisheries, 16, 4-21.  Nei M, Tajima F (1981) Genetic drift and estimation of effective population size. Genetics, 98, 625-640.  Paetkau D, Slade R, Burden M, Estoup A (2004) Genetic assignment methods for the direct, real-time estimation of migration rate: a simulation-based exploration of accuracy and power. Molecular Ecology, 13, 55-65.  Peel D, Ovenden JR, Peel SL (2004) NEESTIMATOR: software for estimating effective population size Version 1.3. Queensland Government, Department of Primary Industries and Fisheries.  Piry S, Alapetite A, Cornuet JM (2004) GENECLASS2: A software for genetic assignment and first-generation migrant detection. Journal of Heredity, 95, 536-539.   Pollock E (1983) A new method for estimating effective population size from allele frequency changes. Genetics, 104, 531-548.   Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes. Proceedings of the National Academy of Sciences USA, 94, 9197-9221.  Ricker WE (1940) On the origin of kokanee: a freshwater type of sockeye salmon. Proceedings of the Royal Society of Canada, 34, 121-135.  Scribner KT, Crane PA, Spearman WJ, Seeb LW (1998) DNA and allozyme markers provide concordant estimates of population differentiation: analyses of US and Canadian populations of Yukon River fall-run chum salmon. Canadian Journal of Fisheries and Aquatic Science, 55, 1748-1758.  Shepherd BG (2000) A case history: the kokanee stocks of Okanagan Lake. Pages 609-616 in Proceedings of a conference on the biology and management of species and habitats at risk, editor L.M. Darling. Kamloops, B.C.     72
Taylor EB, Harvey S, Pollard S, Volpe S (1997) Postglacial genetic differentiation of reproductive ecotypes of kokanee Oncorhynchus nerka in Okanagan Lake, British Columbia. Molecular Ecology, 6, 503-517.  Taylor EB, Kuiper A, Troffe PM, Hoysak D, Pollard S (2000) Variation in developmental biology and microsatellite DNA in reproductive ecotypes of kokanee, Oncorhynchus nerka: Implications for declining populations in a large British Columbia lake. Conservation Genetics, 1, 231-249.  Utter FM, Seeb JE, Seeb LW (1993) Complementary uses of ecological and biochemical-genetic data in identifying and conserving salmon populations. Fisheries Research, 18, 59-76.   Vasemägi A, Nilsson J, Primmer CR (2005c) Expressed sequence tag-linked microsatellites as a source of gene-associated polymorphisms for detecting signatures of divergent selection in Atlantic salmon (Salmo salar L.). Molecular Biology and Evolution, 22, 1067-1076.  Waples RS (1989) A generalized approach for estimating effective population size from temporal changes in allele frequency. Genetics, 121, 379-391.  Waples RS (2006) A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Conservation Genetics, 7, 167-184.  Waples RS, Do C (2008)  LDNE: a program for estimating effective population size from data on linkage disequilibrium. Molecular Ecology Resources, 8, 753-756.  Wenne R, Boudry P, Hemmer-Hansen J, Lubieniecki KP, Was A, Kause A (2007) What role for genomics in fisheries management and aquaculture? Aquatic Living Resources, 20,  241-255.  Winans GA, Pollard S, Kuligowski DR (2003) Two reproductive life history types of kokanee, Oncorhynchus nerka, exhibit multivariate morphometric and protein genetic differentiation. Environmental Biology of Fishes, 77, 87-100.  Winans GA, Paquin MZ, Van Doornik DM, Rawding D, Baker B, Marshall A, Moran P, Kalinowski ST (2004) Genetic stock identification of steelhead in the Columbia River basin: an evaluation of different molecular markers. North American Journal of Fisheries Management, 24, 672-685.  Withler RE (2005) Microsatellite analysis of stream and beach spawning kokanee in Okanagan Lake, British Columbia. Unpublished report. 12 pp.  Wood CC (1995) Life history variation and population structure in sockeye salmon. American Fisheries Society Symposium, 17, 195-216.   73
4 CONCLUSIONS    4.1 Population genomics and conservation    Population genomics offers great potential for enhancing biodiversity and conservation initiatives by facilitating the rapid discovery of genes of adaptive significance and improving estimates of fundamental population parameters (e.g., population abundance).  The genomic techniques used in this thesis detected multiple loci displaying outlier behaviour and illustrated their application to fisheries management in a natural population of conservation concern.  In addition to their usefulness described in this thesis, outlier loci have other practical applications that are beneficial to species and populations of conservation interest.  First, they can be used to prioritize wildlife populations for conservation in which the highest ranking is given to distinct populations with high degrees of adaptive and neutral divergence (Crandall et al. 2000, Moritz 2002, Luikart et al. 2003).  Second, loci that deviate significantly from the rest of the genome can also be used to detect outlier individuals in a population or sample, such as immigrants, illegally trafficked animals or fraudulent food products (Luikart et al. 2003).   4.1.1 Local implications for Okanagan Lake kokanee  The Okanagan Lake kokanee are currently the focus of significant conservation efforts.  Population genomics techniques were employed in this thesis to investigate and elucidate the genetic basis of adaptation in two sympatric ecotypes through outlier loci detection.  Outlier loci representing adaptive genetic markers shed light on gene regions likely influenced by divergent selection in response to two reproductive strategies, which was supported by morphological, behavioural and life history characteristics.  The use of outlier loci for individual assignment to   74
source ecotype and composition estimates of each ecotype in a mixed sample was superior to all other classes of markers and demonstrated the potential to improve the accuracy associated with conventional approaches to kokanee fishery management.  A genetics-based approach to fishery management in the Okanagan Lake population may prove to be the better alternative to achieving an effective and sustainable kokanee fishery.  The outlier loci identified by population genomics techniques may also indicate gene regions that will play a critical role in the future survival of the Okanagan Lake kokanee in the face of environmental change.    4.2 Significance of Thesis Research  The Okanagan Lake kokanee continue to capture the interest of fishing enthusiasts and the local community.  Tours administered by the Environmental Education Centre for the Okanagan inform school and community groups about the life history of stream spawning kokanee at Mission Creek Park and Hardy Falls each fall spawning season.  As a popular location for recreational activity, human-induced disturbances will continue to affect critical kokanee stream spawning habitat and natural shore spawning habitat in Okanagan Lake and its associated tributaries (Northcote & Northcote 1996).  Although the Okanagan Lake kokanee remains a species of major conservation concern, the population has experienced a gradual increase in abundance in recent years initiating the reintroduction of a limited trial fishery in 2005.  The proposed catch limits were based on visual population estimates of shore and stream spawners conducted during the spawning season.  The employment of outlier loci in a genetics-based approach to fishery management will improve the accuracy of stream and shore spawner abundance estimates and will benefit this local fish population through its application to stock   75
assessment, long-term ecological monitoring and the evaluation of current fishery management practices.  This work broke new ground in applying population genomics tools to fisheries management.  These findings have numerous beneficial applications to fisheries management initiatives including the increased flexibility for sampling and enumeration efforts as well as increased resources for setting catch limits within sustainable levels.  They can also benefit this population by prioritizing areas of most concern to inform environmental assessments associated with the development of water interface property on the lake shoreline and along stream edges.  The fitness and genetic integrity of this unique salmonid population as well as the habitat they share with visitors and residents of the central Okanagan will benefit substantially from the development and application of cutting-edge genetic tools and procedures introduced in this thesis.   4.3 Limitations of thesis research  Due to the direct linkage of the EST-linked microsatellite markers with known coding regions (Vasemägi et al. 2005a, Miller et al. 1997), detected outlier loci suggest specific gene regions that are undergoing divergent selection between the two ecotypes of Okanagan Lake kokanee.  However, it is difficult to verify whether outlier loci are genuinely adaptive (Luikart et al. 2003).  Repeated in situ or ex situ experimentation in replicate populations or separate geographic locations can help establish the adaptive importance of a gene as demonstrated by Wilding et al. (2001) and Storz et al. (2003).  In order to understand the function of adaptive loci, comparative analyses were performed with web-based genomic resources available for salmonids, namely the consortium for Genomics Research on All Salmon (cGRASP) but it was   76
not possible to clearly pinpoint the functionality of detected outlier loci (e.g., metabolic genes, reproductive genes, disease-resistance genes).  Further investigation of the DNA sequences of all outlier loci may be useful for inferring biotic or abiotic factors that are driving the physiological, morphological and behavioural divergences observed in both kokanee ecotypes.  Lastly, expansion of the baseline dataset of 95 individuals may enhance the accuracy of individual assignment and mixture composition analyses allowing the evaluation of unknown individuals.   4.4 Future work  Nuclear microsatellites are one of the most frequently used genetic markers in current molecular ecology studies (Schlötterer 2004, Barbara et al. 2007).  Likewise, ESTs are gaining popularity as universal genetic markers in various study systems largely because they are easily transferrable between species that are closely related (Bonin 2008). Therefore, it would be useful to explore the cross species amplification potential of the seven outlier loci found to be informative between ecotypes of Okanagan Lake kokanee to compare with other salmonid populations also displaying this unique adaptation to shore spawning environments.  It would also be useful to test for repeatability and confirm patterns of outlier loci in other kokanee populations.  For example, resident kokanee in the nearby Wood Lake display the same morphological, behavioural and life history ecotype characteristics as seen in Okanagan Lake, and are also of high fisheries management priority to the BC MOE.  Genetic research on other populations displaying similar reproductive adaptation to a shore spawning environment could provide further insight into the mechanisms involved in population divergence of ecotypes.    77
In addition, the combined approaches of population genomics and qualitative genetics (e.g., QTL mapping) may facilitate a more powerful and promising way to tease apart genes underlying adaptive phenotypic variation (Stinchcombe & Hoekstra 2008).  As such, a comprehensive linkage map has been developed for Atlantic salmon using AFLPs (Moen et al. 2004).  The imminent description of the complete genome sequence for salmonids will improve the identification of ecologically and economically important genes (Ng et al. 2005, Wenne et al. 2007).  At this point, it will be possible to accurately map the putatively adaptive gene regions identified in this thesis, potentially providing their function, and further elucidating their role in the divergence of the two Okanagan Lake kokanee ecotypes.    78
4.5 References  Barbara T, Palma-Silva C, Paggi GM, Bered F, Fay MF, Lexer C (2007) Cross-species transfer of nuclear microsatellite markers: potential and limitations. Molecular Ecology, 16, 3759-3767.  Bonin A (2008) Population genomics: a new generation of genome scans to bridge the gap with functional genomics. Molecular Ecology, 17, 3583-3584.  Crandall KA, Bininda-Emonds ORP, Mace GM, Wayne RK (2000) Considering evolutionary processes in conservation biology. Trends in Ecology and Evolution, 15, 290-295.  Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2003) The power and promise of population genomics: from genotyping to genome typing. Nature Reviews Genetics, 4, 981-994.  Miller KM, Withler RE, Beacham RD (1997) Molecular evolution at Mhc genes in two populations of chinook salmon Oncorhynchus tshawytscha. Molecular Ecology, 6,     937-954.  Moen T, Hoyheim B, Munck H, Gomez-Raya L (2004) A linkage map of Atlantic salmon  (Salmo salar) reveals an uncommonly large difference in recombination rate between the  sexes. Animal Genetics, 35, 81-92.   Moritz C (2002) Strategies to protect biological diversity and the evolutionary processes that sustain it. Systematic Biology, 51, 238-254.  Ng SHS, Artieri CG, Bosdet IE, Chiu R, Danzmann RG, Davidson WS, Ferguson MM, Fjell CD, Hoyheim B, Jones SJM, de Jong PJ, Koop BF, Krzywinski MI, Lubieniecki K, Marra MA, Mitchell LA, Mathewson C, Osoegawa K, Parisotto SE, Phillips RB, Rise ML, Von Schalburg KR, Schein JE, Shin H, Siddiqui A, Thorsen J, Wye N, Yang G, Zhu B (2005) A physical map of the genome of Atlantic salmon, Salmo salar. Genomics, 86, 396-404.  Northcote TG, Northcote H (1996) Shoreline marshes of Okanagan Lake: are they habitats of high productivity, diversity, scarcity and vulnerability? Lakes and Reservoirs, Research and Management, 2, 157-161.  Schlötterer C (2004) The evolution of molecular markers – just a matter of fashion? Nature Reviews Genetics, 5, 63-69.  Stinchcombe JR, Hoekstra HE (2008) Combining population genomics and quantitative genetics: finding the genes underlying ecologically important traits. Heredity, 100,  158-170.  Storz JF, Nachman MW (2003) Natural selection on protein polymorphism in the rodent genus Peromyscus: evidence from interlocus contrasts. Evolution, 57, 2628-2635.   79
Vasemägi A, Nilsson J, Primmer CR (2005a) Seventy-five EST-linked Atlantic salmon (Salmo salar L.) microsatellite markers and their cross-amplification in five salmonid species. Molecular Ecology Notes, 5, 282-288.  Wenne R, Boudry P, Hemmer-Hansen J, Lubieniecki KP, Was A, Kause A (2007) What role for genomics in fisheries management and aquaculture? Aquatic Living Resources, 20,  241-255.  Wilding CS, Butlin RK, Grahame J (2001) Differential gene exchange between parapatric morphs of Littorina saxatilis detected using AFLP makers. Journal of Evolutionary Biology, 14, 611-619.    80
APPENDICES   Appendix A Additional data for candidate genes  Table A.1 Description of 243 genetic markers screened for use in this study.  Primer name, primer sequence, marker type and references are listed for each locus.  Marker type denoted by  1 for EST-linked microsatellite, 2 for putatively neutral non-EST-linked microsatellite markers.   Primer name Primer sequence 5'-3' Marker type Reference CA046540M13F TCCCAGTCACGACGTTCCACTATATGATCACAATAACCTTTT 1 Vasemägi et al, 2005 CA046540R GTTTCCAGACACAAATGAGAGACC   CA048828M13F TCCCAGTCACGACGTGAGGGCTTCCCATACAACAA 1 Vasemägi et al, 2005 CA048828R GTTTAAGCGGTGAGTTGACGAGAG   CA048687M13F TCCCAGTCACGACGTCAGAGACAGAGGGTCAGCCTA 1 Vasemägi et al, 2005 CA048687R GTTTCCCATCATCGTAGTCCACA   CA047718M13F TCCCAGTCACGACGTATTTACCGCCTGGTGATGTC 1 Vasemägi et al, 2005 CA047718R GTTTGCAAAGCCCTCATGTTGATT   CA047220M13F TCCCAGTCACGACGTAGCGTTTACGTCGAATCCAA 1 Vasemägi et al, 2005 CA047220R GTTTCTCATGGAGGGTGGAAGTGT   CA047146M13F TCCCAGTCACGACGTAACAGAGATGGAAACCAGCA 1 Vasemägi et al, 2005 CA047146R GTTTCCCAATCTAGCAGGGGAAAT   CA044002M13F TCCCAGTCACGACGTTTCTTGAGATGCCACACCTG 1 Vasemägi et al, 2005 CA044002R GTTTGTTGTTCCCATGTTTCACGA   CA042613M13F TCCCAGTCACGACGTGCCAAGTGTCTTCCTGTGAAA 1 Vasemägi et al, 2005 CA042613R GTTTCAGTCCACCTCGGAAAATC   CA041953M13F TCCCAGTCACGACGTTCACAGTCGCAGGAGGTAAG 1 Vasemägi et al, 2005 CA041953R GTTTTGACCCAGAGACAGATGACCT   CA042465M13F TCCCAGTCACGACGTCAGGAAAAGCCCATTGAGAC 1 Vasemägi et al, 2005 CA042465R GTTTCATTGTTTCATAACGCACCA   CA061261M13F TCCCAGTCACGACGTGGAGTTATCAGGTGGGCAAA 1 Vasemägi et al, 2005 CA061261R GTTTGCGGAGACCGTTTGGTATTA   CA039983M13F TCCCAGTCACGACGTGCGGCCCTTAGTGTAATCAA 1 Vasemägi et al, 2005 CA039983R GTTTCTCGCCAGTCACTCTTCAA   CA039543M13F TCCCAGTCACGACGTCACACAAGATTGGATTGAGCTT 1 Vasemägi et al, 2005 CA039543R GTTTCTCCCTTGTTTTTCCCCAAT   CA039240M13F TCCCAGTCACGACGTGCTGACCTAAATAACAGTTTGGTGT 1 Vasemägi et al, 2005 CA039240R GTTTGGAAATCCCTCAAACCCTTC   CB514761M13F TCCCAGTCACGACGTCTGTCCTTGGGCACATTTTT 1 Vasemägi et al, 2005 CB514761R GTTTACAGCTCTGGTTCCGACA   CA053293M13F TCCCAGTCACGACGTTCTCATGGTGAGCAACAAACA 1 Vasemägi et al, 2005 CA053293R GTTTACTCTGGGGCATTCATTCAG     81
Primer name Primer sequence 5'-3' Marker type Reference CA040282M13F TCCCAGTCACGACGTTGCAAGTAAAGGCAGGGTTT 1 Vasemägi et al, 2005 CA040282R GTTTGTGGTAGGATTGGGGTTCCT    CA040580M13F TCCCAGTCACGACGTTCAATGGGGAACAATAACAACA 1 Vasemägi et al, 2005 CA040580R GTTTGGTCTGTTCCCCTCTGTTT   CA047944M13F TCCCAGTCACGACGTGCCGCCCAGATTATCAGTAA 1 Vasemägi et al, 2005 CA047944R GTTTGTTTCCAAACCAAAAACTGAA   CA054538M13F TCCCAGTCACGACGTAGCTACTGGTCCCCAAACCT 1 Vasemägi et al, 2005 CA054538R GTTTAAGGTGGACTTGGCTTGATG   CA054957M13F TCCCAGTCACGACGTGGGTCATTTGGGACACAGTT 1 Vasemägi et al, 2005 CA054957R GTTTGGAGACAACGAGGAGAGTCG    CA055873M13F TCCCAGTCACGACGTGCACAGAGCTGGGTTCAGTA 1 Vasemägi et al, 2005 CA055873R GTTTGTGTTTGGGAGACTGGTCA   CA058128M13F TCCCAGTCACGACGTGCACTGTATTTTGGTCTCCACA 1 Vasemägi et al, 2005 CA058128R GTTTCTGTTTCCCATCTTCATTGC   CA058580M13F TCCCAGTCACGACGTATAACATGCAAGCGGTTTCC 1 Vasemägi et al, 2005 CA058580R GTTTGCTGGAAGTGTTGAGTTGC   CA059521M13F TCCCAGTCACGACGTGGCGTCAAGTCTGTCACTCA 1 Vasemägi et al, 2005 CA059521R GTTTCGATAACGTGACCAATGCAC   CA056586M13F TCCCAGTCACGACGTGCCTACATCGCACACCATAA 1 Vasemägi et al, 2005 CA056586R GTTTCCACTCATTCTCGCTTTTCA   CA038562M13F TCCCAGTCACGACGTTAGTGGCTCCATCCATTGGT 1 Vasemägi et al, 2005 CA038562R GTTTCATTGGCTTTCCAGAGGTC   CA058557M13F TCCCAGTCACGACGTTCCAACACCACAATCTTCAAGT 1 Vasemägi et al, 2005 CA058557R GTTTGTCGTTTCGGGTGTAAATG   CA062844M13F TCCCAGTCACGACGTTGACACTGTGGCCTGTCTCT  1 Vasemägi et al, 2005 CA062844R GTTTGAGTTCTGGGTTATTTATTCACA   CA057681M13F TCCCAGTCACGACGTCTATTGCTCCCCGATGTTGT 1 Vasemägi et al, 2005 CA057681R GTTTCCATCCAGGCTTCTTATTTCA   CA062621M13F TCCCAGTCACGACGTTTAAAACTCCTGCCCTGTGG 1 Vasemägi et al, 2005 CA062621R GTTTCTTCCAAGGCTTGATGTCC   CA062068M13F TCCCAGTCACGACGTTCATCAAGGCTTTGTTGCAG 1 Vasemägi et al, 2005 CA062068R GTTTGTGCCAAATTATTGGCCGTA   CA059136M13F TCCCAGTCACGACGTAGGGTAGTGAGAAAGCAGCAA 1 Vasemägi et al, 2005 CA059136R GTTTAACTGGCTGGCCATAGG   CA058902M13F TCCCAGTCACGACGTCCAGCCAGAGAGGAACAGAC 1 Vasemägi et al, 2005 CA058902R GTTTGATAGCCCATCACACCAACC   CA055420M13F TCCCAGTCACGACGTAGGCAGTATTTGGCGACATC 1 Vasemägi et al, 2005 CA055420R GTTTGCTGCTTCCCTTATGTCCTG   CA054978M13F TCCCAGTCACGACGTACACAACCCAGACACCAACA 1 Vasemägi et al, 2005 CA054978R GTTTTCTGCCCTTCCTGTCCTAA   CA055301M13F TCCCAGTCACGACGTAGAACCAAGGGTACCGATCC 1 Vasemägi et al, 2005 CA055301R GTTTGGGAAATGGGTGGTAAGAAAA         82
Primer name Primer sequence 5'-3' Marker type Reference CA050376M13F TCCCAGTCACGACGTAGAGAACCATGAGGGGGAAC 1 Vasemägi et al, 2005 CA050376R GTTTCTTTCTGATGTGGGATGGA   BG935488M13F TCCCAGTCACGACGTTGACCCCACCAAGTTTTTCT 1 Vasemägi et al, 2005 BG935488R GTTTAAACACAGTAAGCCCATCTATTG   B1FA  GGTCTTGACTTG[AC]TCAGTCA MHC class II Miller et al, 1997 B1RA CCGATACTCCTCAAAGGACCTGCA   MHCIM13F TCCCAGTCACGACGTGGAGAGCTGCCCAGATGACTT MHC class I Grimholt and Drablos, 2002 MHCIR GTTTCAATTACCACAAGCCCGCTC   TAP2BM13F TCCCAGTCACGACGTGCGGGACACCGTCAGGGCAGT TAP2B Grimholt and Drablos, 2002 TAP2BR GTTTCCTGATATTGTCTGCCAGATC   CB512797M13F TCCCAGTCACGACGTGGACGAAGGACCACTCCAAT 1 Vasemägi et al, 2005 CB512797R GTTTGGGGGTGCTGAGGAGTATTT   CB17778M13F TCCCAGTCACGACGTCCCAGCTGAGGCTCTTTATG 1 Vasemägi et al, 2005 CB17778R GTTTCCTCCACATGTTCGTCA   CA767838M13F TCCCAGTCACGACGTGCAGGGAGAGATCGAGAACA 1 Vasemägi et al, 2005 CA767838R GTTTTTGACTGCTGGCTGATG   CA050122M13F TCCCAGTCACGACGTTCCAAGGCGTTCATGTGTTA 1 Vasemägi et al, 2005 CA050122R GTTTCCCAGTCTCCCTCTGCTTAG   OMM5000M13F TCCCAGTCACGACGTAACAGAGCAGTGAGGGGACTGAGA 1 Rexroad et al, 2005 OMM5000R GTTTCAAGTGATGTTGGTGCGAGGG   OMM5001M13F TCCCAGTCACGACGTGAAGTCTCAGCCAGTGGTAAAGTC 1 Rexroad et al, 2005 OMM5001R GTTTCCCTTTACAAGCCTGATGTCAT   OMM5002M13F TCCCAGTCACGACGTGGGCTTCCTGGAGGACTACTTTA 1 Rexroad et al, 2005 OMM5002R GTTTGCCCTGACAGACAGCAACATATAG   OMM5003M13F TCCCAGTCACGACGTTCTAGGCTGGTCCTTGTAGGTTGT 1 Rexroad et al, 2005 OMM5003R GTTTTCCTGGAGAATGTCAAGTCCGT   OMM5004M13F TCCCAGTCACGACGTAGCTTGTATTAAAGAGGCTTGAAA 1 Rexroad et al, 2005 OMM5004R GTTTCAAATTGGACCGCACACTA   OMM5005M13F TCCCAGTCACGACGTTGACCAGCACCTCCTTATACCTC 1 Rexroad et al, 2005 OMM5005R GTTTGGCATCAACCAAGAGCTAAACCAA   OMM5006M13F TCCCAGTCACGACGTAAGGCATCATTGGTGATAACAAGG 1 Rexroad et al, 2005 OMM5006R GTTTGGGACGATGCTTTGGCTAAGA   OMM5007M13F TCCCAGTCACGACGTAGATGCCTGTCGAGTGTTG 1 Rexroad et al, 2005 OMM5007R GTTTGAGGAGCATCATTTAGAGACTACA   OMM5008M13F TCCCAGTCACGACGTCTGTTTCGTTGTCCTCATATCAACC 1 Rexroad et al, 2005 OMM5008R GTTTTCCATTATCCAATCAGGAGAGCTCTAT   OMM5010M13F TCCCAGTCACGACGTCCCAACTTGCTGTCCCTAGA 1 Rexroad et al, 2005 OMM5010R GTTTTAACCCACAGAAAGAAGGTGTTGT   OMM5011M13F TCCCAGTCACGACGTGGCACCCAGCAAGTGATCTACTTC 1 Rexroad et al, 2005 OMM5011R GTTTCTGTGCGTTCCCAGTGGACTCT   OMM5012M13F TCCCAGTCACGACGTAGGACCCCACCCACAC 1 Rexroad et al, 2005 OMM5012R GTTTTTGGAGGGTCTATGCTCG     83
Primer name Primer sequence 5'-3' Marker type Reference OMM5020M13F TCCCAGTCACGACGTAGCCAGCCGGTCTGTTTAGTC 1 Rexroad et al, 2005 OMM5020R GTTTTGGCCCCATAACACAGGAGTATTA   OMM5030M13F TCCCAGTCACGACGTGGGTGCTCTACACACATACACAAT 1 Rexroad et al, 2005 OMM5030R GTTTTGGAGGTATCTTGTGGTCAGTCT   OMM5031M13F TCCCAGTCACGACGTGAAACCTCCCCATATCATTG 1 Rexroad et al, 2005 OMM5031R GTTTCGGCAGAATATCTCCATAAGT   OMM5032M13F TCCCAGTCACGACGTGCCTTCCAATGATCCTAGAG 1 Rexroad et al, 2005 OMM5032R GTTTCCACACTATGTGACCCTCACT   OMM5033M13F TCCCAGTCACGACGTTGGTCAGATATACAGCTCCAACCTC 1 Rexroad et al, 2005 OMM5033R GTTTACATGTAAGCCAATCAACACATAGACT   OMM5034M13F TCCCAGTCACGACGTACAAAGCTGCCATCAGATAACATC 1 Rexroad et al, 2005 OMM5034R GTTTGTACGGTTGAATTGAGCCAGTCTA   OMM5037M13F TCCCAGTCACGACGTACCGAGGTAGAGGAAGAAGCTTTA 1 Rexroad et al, 2005 OMM5037R GTTTCTCATAGACAAAGCCTTGAACGAG   OMM5039M13F TCCCAGTCACGACGTACAGAAATGGTTCAGGTCCTTCATAGT 1 Rexroad et al, 2005 OMM5039R GTTTCAGCAGCAAGAGTTCTTCAGAACAC   OMM5013M13F TCCCAGTCACGACGTAGGGTACAGGAGGTAAACAG 1 Rexroad et al, 2005 OMM5013MR GTTTAAACTGATATGGAGGTTGAAC   OMM5017M13F TCCCAGTCACGACGTTTGAGCCAAACATGCCTC 1 Rexroad et al, 2005 OMM5017MR GTTTCACAGCATCTAGACAGTTCCC   OMM5023M13F TCCCAGTCACGACGTATTAGGCAACCCTCATGAACTGTA 1 Rexroad et al, 2005 OMM5023MR GTTTGCCATTGAGTGTCTTTAAGCAC   OMM5025M13F TCCCAGTCACGACGTGGAGGACAGTTAAAGGCATCGCGCTTC 1 Rexroad et al, 2005 OMM5025MR GTTTCGTCATATCTTCAAGCTCCGCGAGAGG   OMM5029M13F TCCCAGTCACGACGTTGCTCCCTCTGGACTATCTAGCCT 1 Rexroad et al, 2005 OMM5029MR GTTTGACGCACACAGACATATACATGCC   OMM5041M13F TCCCAGTCACGACGTCTGGGGACTCTTGGAACT 1 Rexroad et al, 2005 OMM5041MR GTTTAGCGTAAAGCGTCATGG   OMM5043M13F TCCCAGTCACGACGTAACTGTTTTTGCCTCCACCCTAT 1 Rexroad et al, 2005 OMM5043MR GTTTGCTCCCTCCTTTGTTTATTTTTAGGTT   OMM5047M13F TCCCAGTCACGACGTACTTTCAGCAGCATCTGGTCA 1 Rexroad et al, 2005 OMM5047MR GTTTCCTGGTCCTCAGCGTTCAT   OMM5050M13F TCCCAGTCACGACGTACAACTGGAATAGGAACGCAAAGC 1 Rexroad et al, 2005 OMM5050MR GTTTCAATTCTCTCATCCTCGCCACTC   OMM5053M13F TCCCAGTCACGACGTTCTATGGACAAACTGGGAGTAAATGCC 1 Rexroad et al, 2005 OMM5053MR GTTTGCCCTCTCATTGTGTTTCTGTATAGCC   OMM5054M13F TCCCAGTCACGACGTGTTTCTATTTCCACCCTCACCGCTGAT 1 Rexroad et al, 2005 OMM5054MR GTTTGGGCAAAATTTCTTGTCAAGCCAACC   OMM5055M13F TCCCAGTCACGACGTGGAACAGGGCTGCATTAGCTTTG 1 Rexroad et al, 2005 OMM5055MR GTTTCGCCATAGGTCTTGTTCAGGTACA   OMM5058M13F TCCCAGTCACGACGTCACCCATCAGATTTGTAAGAGCGT 1 Rexroad et al, 2005 OMM5058MR GTTTCCTATGCGTTTGCTTTCGTG         84
Primer name Primer sequence 5'-3' Marker type Reference OMM5059M13F TCCCAGTCACGACGTCCGCAGCTTCCGTTCTACTCC 1 Rexroad et al, 2005 OMM5059MR GTTTGACTACCCATGATGCACGGC   OMM5060M13F TCCCAGTCACGACGTTCTCGGGCCAAACCTTCTTATTGC 1 Rexroad et al, 2005 OMM5060MR GTTTAGCCACTACATCTCCACGCCCTT   OMM5061M13F TCCCAGTCACGACGTGCGTTGGGAGAGAACAATACC 1 Rexroad et al, 2005 OMM5061MR GTTTCCCATCACACCAGTTGCC   OMM5062M13F TCCCAGTCACGACGTTGCAGTAACCTGAAGGTCCAATGGG 1 Rexroad et al, 2005 OMM5062MR GTTTAAGGAGGAGGGAGAGAGCTACGGAGAC   OMM5063M13F TCCCAGTCACGACGTGGGGTGATGATGGAAAGATTAGTG 1 Rexroad et al, 2005 OMM5063MR GTTTGGGAAAAATAACTGATGGACGGT   OMM5064M13F TCCCAGTCACGACGTTGTCTGAAGAACCTGCCTATCTGT 1 Rexroad et al, 2005 OMM5064MR GTTTGCTGGTGCTGACCTTGGTAGTG   OMM5067M13F TCCCAGTCACGACGTCATAAGCAGAAATCAGGGGTAACA 1 Rexroad et al, 2005 OMM5067MR GTTTGGGCATAAGCTATGTAATTTACGC   OMM5072M13F TCCCAGTCACGACGTAGACAGCCAACCACTGATACC 1 Rexroad et al, 2005 OMM5072MR GTTTAGAAACATGGCAAACGATGA   OMM5074M13F TCCCAGTCACGACGTTCGCTTTGGGTAGAAGTTGCCTTTAAC 1 Rexroad et al, 2005 OMM5074MR GTTTAACATTAAGAACGAGTGGAATCACGC   OMM5075M13F TCCCAGTCACGACGTAGATTCACCACGTATCACGGAATG 1 Rexroad et al, 2005 OMM5075MR GTTTCTTCACATCCTACTCAATGATCGACC   OMM5077M13F TCCCAGTCACGACGTTATGCGCTAGACTACAACGAG 1 Rexroad et al, 2005 OMM5077MR GTTTCTGAGGGGCAATGTAAGT   OMM5088M13F TCCCAGTCACGACGTATCTCTCGTCTTTCCTGTCTTCGT 1 Rexroad et al, 2005 OMM5088MR GTTTACTCTGGCTGTGCATTGTGG   OMM5090M13F TCCCAGTCACGACGTAACAAGGCAAGCAACAAGCATAAC 1 Rexroad et al, 2005 OMM5090MR GTTTGGGAGCAGGTATCTTTCGGTCT   OMM5091M13F TCCCAGTCACGACGTGCAGGAAAAACACCCAGATACAA 1 Rexroad et al, 2005 OMM5091MR GTTTACACTGGCTGGTGTCGTTACATTA   OMM5093M13F TCCCAGTCACGACGTTTAATCACACAGCATTAGTAGTCAGCC 1 Rexroad et al, 2005 OMM5093MR GTTTGAGTTAGATGACCTGGTTAATGCC   OMM5099M13F TCCCAGTCACGACGTGAATCGACCAACAAGACCATC 1 Rexroad et al, 2005 OMM5099MR GTTTGGCACAGAAAAGACGTACA   OMM5100M13F TCCCAGTCACGACGTTGCTGGATACTGGAGCTACTT 1 Rexroad et al, 2005 OMM5100MR GTTTCTGGTTTCAGGGACTGC   OMM5106M13F TCCCAGTCACGACGTGGTATGATGCCTCTGAATGAACAGTAT 1 Rexroad et al, 2005 OMM5106MR GTTTACCAGTTGGTGTTTAACTCATATCAGC   OMM5107M13F TCCCAGTCACGACGTAGAGCAACAAATCCACGTGAG 1 Rexroad et al, 2005 OMM5107MR GTTTATCAGCCATAACTGCCATACTGTA   OMM5108M13F TCCCAGTCACGACGTACACTATCCCATAGTTGACTGACG 1 Rexroad et al, 2005 OMM5108MR GTTTCTGGAGAAATGTGGCACAATAA   OMM5109M13F TCCCAGTCACGACGTGTTTCACAAAGTCATAACGAGCAG 1 Rexroad et al, 2005 OMM5109MR GTTTGTTGGCACAAGGCATTATACC         85
Primer name Primer sequence 5'-3' Marker type Reference OMM5112M13F TCCCAGTCACGACGTCCCTTCCCACAATCCTGACCTTA 1 Rexroad et al, 2005 OMM5112MR GTTTCATGCTGATCCTGCTGGTACTCCT   OMM5117M13F TCCCAGTCACGACGTGCGAGTGCAAGAGGAAACAAGTAC 1 Rexroad et al, 2005 OMM5117MR GTTTCGCAGAGCCATTGAGAGATACTGT   OMM5121M13F TCCCAGTCACGACGTCTTCAGGCTTTATTGTGTTTGACATGC 1 Rexroad et al, 2005 OMM5121MR GTTTGCCAACATGTAACACCACTAGCTGC   OMM5124M13F TCCCAGTCACGACGTTCTCTTTGTAACGTAGTGCTGCGAGTG 1 Rexroad et al, 2005 OMM5124MR GTTTGTGGAGGTGACTGTCGGGTATCT   OMM5125M13F TCCCAGTCACGACGTAAAGCCCTCATTGTGATAACACTG 1 Rexroad et al, 2005 OMM5125MR GTTTGCGCTGTGTAGAACGGAATC   OMM5126M13F TCCCAGTCACGACGTCACACTATTTGGGACGCACA 1 Rexroad et al, 2005 OMM5126MR GTTTCCCAACAGTTGAGTCTCCATAGTT   EV374295M13F TCCCAGTCACGACGTATGCATTTAGCAGTGGCGG 1 Koop et al. 2008 EV374295MR GTTTACGGGAGTTGTAGCGATGAG   EV374296M13F TCCCAGTCACGACGTCTTCGTCTCTCCCGAGTCC 1 Koop et al. 2008 EV374296MR GTTTATGCATTTAGCAGTGGCGG   EV374384M13F TCCCAGTCACGACGTGCTCAGTGAAGGGAATGGC 1 Koop et al. 2008 EV374384MR GTTTAGCTCAGGATGTACGCAGG   EV374388M13F TCCCAGTCACGACGTTGGCTTTGTGTATTGGGGC 1 Koop et al. 2008 EV374388MR GTTTCTTGGCAGGGTCGTGTTTC   EV374673M13F TCCCAGTCACGACGTCACTGCAAGTGTGTCTGTC 1 Koop et al. 2008 EV374673MR GTTTATGCAGAGAGACCCGGAAC   EV374740M13F TCCCAGTCACGACGTGGATCCCAACAAACTATAGCCG 1 Koop et al. 2008 EV374740MR GTTTAGCCGATCACATCCTCATCC   EV374869M13F TCCCAGTCACGACGTGGGGAGAATCAACCTGGGG 1 Koop et al. 2008 EV374869MR GTTTACTTCCTCCTTGTCTGCCG   EV374998M13F TCCCAGTCACGACGTATGGGTTTGACTCCTCCGC 1 Koop et al. 2008 EV374998MR GTTTCAGGGCCAGGAGTATGAGG   EV375011M13F TCCCAGTCACGACGTCAGACCCTGGACCATCTCG 1 Koop et al. 2008 EV375011MR GTTTACGCAGTTACACCTGGGC   EV375068M13F TCCCAGTCACGACGTACAGGATTCAGCCAACACAG 1 Koop et al. 2008 EV375068MR GTTTGAACAGCCTTCCAACAGCG   EV375170M13F TCCCAGTCACGACGTCAGTGTCTTCCGACAAAGGC 1 Koop et al. 2008 EV375170MR GTTTAGCAGCACTTAGAAGACCCC   EV375597M13F TCCCAGTCACGACGTAATGACCTGTCCTGCCCTC 1 Koop et al. 2008 EV375597MR GTTTCCGTTAACCTCTGCCCC   EV375911M13F TCCCAGTCACGACGTACTGTTCCCACTGGCCTTC 1 Koop et al. 2008 EV375911MR GTTTCCTGGTCCTCCCTGTCTC   EV376252M13F TCCCAGTCACGACGTTCACACTACCATGCCTGACC 1 Koop et al. 2008 EV376252MR GTTTGCCCTGCCTTTTCATGGAG   EV376297M13F TCCCAGTCACGACGTCGACGGAGGAAACATGCAG 1 Koop et al. 2008 EV376297MR GTTTACACTGGAACATCGACCCC         86
Primer name Primer sequence 5'-3' Marker type Reference EV376321M13F TCCCAGTCACGACGTACATTCTCTCCAGTTTTCATGTC 1 Koop et al. 2008 EV376321MR GTTTCTGAGTGCAGAGCCTTGG   EV376395M13F TCCCAGTCACGACGTGAACAGAGTGACTGGCAAGG 1 Koop et al. 2008 EV376395MR GTTTGCCAAGTACGGCCTGAATG   EV376475M13F TCCCAGTCACGACGTGTGAAGGCGTCGTTTGACC 1 Koop et al. 2008 EV376475MR GTTTCAACTTCACCGTCTTACCAGC   EV376481M13F TCCCAGTCACGACGTTCACGGCACTGAGGAAGAG 1 Koop et al. 2008 EV376481MR GTTTGCTCAGCATTAGCGCACAG   EV376501M13F TCCCAGTCACGACGTACTTCCAAAAGGGGTGCTG 1 Koop et al. 2008 EV376501MR GTTTGACGGTATGAAGGGCAGG   EV376551M13F TCCCAGTCACGACGTAGTGTGTGCCGTTGTTACC 1 Koop et al. 2008 EV376551MR GTTTGTCCTTCCTTTCCCCTGC   EV376631M13F TCCCAGTCACGACGTACACCTCAGCCATCCATCC 1 Koop et al. 2008 EV376631MR GTTTAAATGGCAGCGACGACAAG   EV376659M13F TCCCAGTCACGACGTCACTCACACAAATACACCAAGC 1 Koop et al. 2008 EV376659MR GTTTCTGAAGGGTACCTGTGAAGC   EV376660M13F TCCCAGTCACGACGTAGACACCAGCCAGACAACC 1 Koop et al. 2008 EV376660MR GTTTCTTTCAGCACCACGAAGCC   EV376670M13F TCCCAGTCACGACGTGCGCTTCAACCAATGAGGG 1 Koop et al. 2008 EV376670MR GTTTAGAGAGACAGCGAAAGGGAC   EV376710M13F TCCCAGTCACGACGTACCCACAACTGACCACAAC 1 Koop et al. 2008 EV376710MR GTTTAGAGGAAACAGACCCCAGC   EV376804M13F TCCCAGTCACGACGTTGCTCTGAACTACACCCACTC 1 Koop et al. 2008 EV376804MR GTTTAAACTGCACCCCGTGTTTG   EV376805M13F TCCCAGTCACGACGTACAGTCAAACTGCACACCG 1 Koop et al. 2008 EV376805MR GTTTGCTCTGAACTACACCCACTC   EV376862M13F TCCCAGTCACGACGTCAGGGTCAGAGAGGATGGC 1 Koop et al. 2008 EV376862MR GTTTAGCAGACCTGAACTGGGTG   EV376893M13F TCCCAGTCACGACGTACCGAGACGACAGAGCATC 1 Koop et al. 2008 EV376893MR GTTTGAAGTGGGATGGGGAAGGG   EV376894M13F TCCCAGTCACGACGTTGGCCAGCATCTACTCTCG 1 Koop et al. 2008 EV376894MR GTTTACCTGTGCCAGTGGGTTTG   EV377149M13F TCCCAGTCACGACGTTCCAACCACTTATCACAATTAACCC 1 Koop et al. 2008 EV377149MR GTTTAGAGAATTGTATGGACTTAGCTTTG   EV377205M13F TCCCAGTCACGACGTTGAGTCTTATACAGCACACACAAC 1 Koop et al. 2008 EV377205MR GTTTGAGCAATCTCAATGGGCCG   EV377249M13F TCCCAGTCACGACGTGACAAGTTCAGCTGGTTCGG 1 Koop et al. 2008 EV377249MR GTTTGGACCTCCTCGGGAAACTC   EV377291M13F TCCCAGTCACGACGTGCACAGCAAGGTAAATCCCC 1 Koop et al. 2008 EV377291MR GTTTACAGTGATGAACAACCACATCC   EV377329M13F TCCCAGTCACGACGTAGAGAGACACAGCCACAGC 1 Koop et al. 2008 EV377329MR GTTTATCTCTGCCACACACCCTC         87
Primer name Primer sequence 5'-3' Marker type Reference EV377365M13F TCCCAGTCACGACGTTCCCAACAGGCTCAACTCC 1 Koop et al. 2008 EV377365MR GTTTACCGAGGAGAGCAACAAGG   EV377417M13F TCCCAGTCACGACGTTGTGTGTGTGGATCTGGGG 1 Koop et al. 2008 EV377417MR GTTTGGTCCAGCAAACCTGAAGC   EV377449M13F TCCCAGTCACGACGTAGGTGAACCTGGAGACACG 1 Koop et al. 2008 EV377449MR GTTTACTGACAGCCTACTGGAGAAC   EV377769M13F TCCCAGTCACGACGTGCTGAAGGTAGTGTTGGGC 1 Koop et al. 2008 EV377769MR GTTTAGCTGGTCGGTGTTCTGG   EV377929M13F TCCCAGTCACGACGTTGTTCGTTCACTGCCATGC 1 Koop et al. 2008 EV377929MR GTTTGTTGCTGTCTGAGGAGTGTG   EV377936M13F TCCCAGTCACGACGTTGAAAAGCTGGGTTTGGGC 1 Koop et al. 2008 EV377936MR GTTTACGACGAGTTTGGTCCCTC   EV378220M13F TCCCAGTCACGACGTTGACACAAGGTTGTCGGTTTC 1 Koop et al. 2008 EV378220MR GTTTCCTCAGGGAGAGAGGGATG   EV378247M13F TCCCAGTCACGACGTACAGAGACCCATGAGGAGC 1 Koop et al. 2008 EV378247MR GTTTGTGTGTCAACAGAGCACGG   EV378520M13F TCCCAGTCACGACGTGGGACAGAGAGAGTGAGAGATG 1 Koop et al. 2008 EV378520MR GTTTCCGTATCCTGTTAGTCGGTC   EV378728M13F TCCCAGTCACGACGTGCTGACAAGTCGAAGCAGG 1 Koop et al. 2008 EV378728MR GTTTCAAGCATGGAAGGAGAGACG   EV378765M13F TCCCAGTCACGACGTGGAAACGAGGGCAAAAGGG 1 Koop et al. 2008 EV378765MR GTTTGTATTGTTGCCACGGATGGG   EV378842M13F TCCCAGTCACGACGTCAACGAGTCACACAGCGAG 1 Koop et al. 2008 EV378842MR GTTTACCTTCGCCTCACCAAGAG   EV378944M13F TCCCAGTCACGACGTAAAGCCACAAGGCCACATC 1 Koop et al. 2008 EV378944MR GTTTCCTCCCTCACCACAGATCG   Ots02ESFUM13F TCCCAGTCACGACGTCAAAACCAGGTACACCATTT 1 Wright et al. 2007 Ots02ESFUMR GTTTGCAAGACTCTCCACAATAGG   Ots05ESFUM13F TCCCAGTCACGACGTGAAATGTCTGGCTCAGTTTG 1 Wright et al. 2007 Ots05ESFUMR GTTTGGGATAAAATGGTGCTACAG   Ots06ESFUM13F TCCCAGTCACGACGTGACATACGCCTAGTTTCCTG 1 Wright et al. 2007 Ots06ESFUMR GTTTCATGGTGGATCAGCTTTAAT   Ots07ESFUM13F TCCCAGTCACGACGTAGCAGTGCATTTTCAGATTT 1 Wright et al. 2007 Ots07ESFUMR GTTTGCCTATGGATTCTGTTTGAA   Ots10ESFUM13F TCCCAGTCACGACGTCAAAACCAGGTACACCATTT 1 Wright et al. 2007 Ots10ESFUMR GTTTCCTCTTCTGTTGGGAGTAA   Ots12ESFUM13F TCCCAGTCACGACGTGGTGGAAGCATTTTTAATTATC 1 Wright et al. 2007 Ots12ESFUMR GTTTGGCCATGGATATTTATTTGA   Ots13ESFUM13F TCCCAGTCACGACGTGTCATCAGAATGATTGGTT 1 Wright et al. 2007 Ots13ESFUMR GTTTGGATCATGGGTAATGTGTTC   Ots14ESFUM13F TCCCAGTCACGACGTTTTCCACCTACTTTCCTTCA 1 Wright et al. 2007 Ots14ESFUMR GTTTGTGCAATATTCAGTCCACCT         88
Primer name Primer sequence 5'-3' Marker type Reference Ots15ESFUM13F TCCCAGTCACGACGTGTGTCAGTCTTGAAAGAGCC 1 Wright et al. 2007 Ots15ESFUMR GTTTAATAGCTCAAACGAGGGAAT   Ots21ESFUM13F TCCCAGTCACGACGTGCAACGACTTTAAAGACACC 1 Wright et al. 2007 Ots21ESFUMR GTTTGCGCTAAGAACTGGACATAG   Ots23ESFUM13F TCCCAGTCACGACGTCCAACACAGTTTGTTCTCCT 1 Wright et al. 2007 Ots23ESFUMR GTTTGCACCTCTAAACAAATCAGG   Ots26ESFUM13F TCCCAGTCACGACGTGGTGTGATTTTTCCCAATAA 1 Wright et al. 2007 Ots26ESFUMR GTTTAAAATGTTTGAGGCAACACT   Ots27ESFUM13F TCCCAGTCACGACGTCCTGGAATGACAAGACCAT 1 Wright et al. 2007 Ots27ESFUMR GTTTACATATCCCGACACTCCTC   Ots29ESFUM13F TCCCAGTCACGACGTACCAACAAGACCATCAAGAG 1 Wright et al. 2007 Ots29ESFUMR GTTTCGTACATAATGGCAGTAGCA   Ots30ESFUM13F TCCCAGTCACGACGTGTCAAATTTGCAACAGCAG 1 Wright et al. 2007 Ots30ESFUMR GTTTGAGATCCCATGTTGATGTTT   CL8689M13F TCCCAGTCACGACGTCAAGCAGGGGTCTTAGTTGA 1 Siemon et al. 2005 CL8689MR GTTTAGGGCTGACCAGATGTTG   CL15841M13F TCCCAGTCACGACGTTATTCAACACTGATGTGAGCC 1 Siemon et al. 2005 CL15841MR GTTTACCTAATCACGAGTTTGTCA   CL17390M13F TCCCAGTCACGACGTTTTCTGTCAACACTGCCTGT 1 Siemon et al. 2005 CL17390MR GTTTAACTTACTTACCCCAGCACG   CL24648M13F TCCCAGTCACGACGTCTAAGTGGTGAAGTGGGATG 1 Siemon et al. 2005 CL24648MR GTTTAAGAACCGCGACGACAGA   EV379176M13F TCCCAGTCACGACGTAGAAGCTGGGTTTGGGCTC 1 Koop et al. 2008 EV379176MR GTTTCGAGTGTGGTCCCTCCATC   EV379360M13F TCCCAGTCACGACGTCAGCACTCTTCCTGGGGTC 1 Koop et al. 2008 EV379360MR GTTTATGTATTTGGCTACGGTGTATG   EV379484M13F TCCCAGTCACGACGTTCCTGGATAGAAGGCGCAG 1 Koop et al. 2008 EV379484MR GTTTGTGACAAAGTGGTAGGAAGCC   EV379588M13F TCCCAGTCACGACGTGCACTAAGATGCAGTGCCG 1 Koop et al. 2008 EV379588MR GTTTGAGAGGCACGGAGGAGATG   EV379691M13F TCCCAGTCACGACGTTCAGGGTCAGGGGTTATGTG 1 Koop et al. 2008 EV379691MR GTTTCAGGCTGCGTATGAATGAGG   EV379703M13F TCCCAGTCACGACGTACACACCATAGGAAATTGACCC 1 Koop et al. 2008 EV379703MR GTTTAACACTGAATAGCATGGCAC   EV379744M13F TCCCAGTCACGACGTAGAAGCTGGGTTTGGGCTC 1 Koop et al. 2008 EV379744MR GTTTGTCTGGTCCCTCCATCGTC   EV379848M13F TCCCAGTCACGACGTGCTACTCTGTCCTCCACTCG 1 Koop et al. 2008 EV379848MR GTTTGGGACTCCTGGAATGTGGG   EV379914M13F TCCCAGTCACGACGTAGAATGGAGGAAAGTGAGGGAG 1 Koop et al. 2008 EV379914MR GTTTCCCATCAGTGGAGTTTGGC   EV379968M13F TCCCAGTCACGACGTACAACCCAGTCAGACCAAAC 1 Koop et al. 2008 EV379968MR GTTTACCACTTAGAACGCATGTCC         89
Primer name Primer sequence 5'-3' Marker type Reference EV379974M13F TCCCAGTCACGACGTAAGGCTACATACAATAAGAGATCAG 1 Koop et al. 2008 EV379974MR GTTTACAGGGAAGCGCTCAGTTC   EV380087M13F TCCCAGTCACGACGTTCTGGCAGCGATAGGAAGG 1 Koop et al. 2008 EV380087MR GTTTGAAGCTTGTGTGCTGCTGG   EV380233M13F TCCCAGTCACGACGTAACTTCACAATGCTCAAAGGG 1 Koop et al. 2008 EV380233MR GTTTAGGTCACTCAGTCGAGCAG   EV380634M13F TCCCAGTCACGACGTACGGGTAGAAAGCAGTCCC 1 Koop et al. 2008 EV380634MR GTTTCACGTGCGATAGAGGTGG   EV380723M13F TCCCAGTCACGACGTACAGGGCATCAAGACTCCG 1 Koop et al. 2008 EV380723MR GTTTCCCCTGGAACAAACACACC   EV380741M13F TCCCAGTCACGACGTGAGAGATGCAGGAGGGTGG 1 Koop et al. 2008 EV380741MR GTTTCAGAGAGGAACCCCAGAGC   EV380751M13F TCCCAGTCACGACGTGCTGGAAGCAGCGAAGAAG 1 Koop et al. 2008 EV380751MR GTTTGCAAGGAGAGAAGGTATGAGG   EV380826M13F TCCCAGTCACGACGTTGGGGACAGCTGAAGATCC 1 Koop et al. 2008 EV380826MR GTTTAGCAACCCCATACCAGGC   EV380892M13F TCCCAGTCACGACGTTGCAGGATATATGGACAATGGG 1 Koop et al. 2008 EV380892MR GTTTCGCCATGATACACAGCCTC   EV381023M13F TCCCAGTCACGACGTTGTAACACAAACACAGCCCC 1 Koop et al. 2008 EV381023MR GTTTACACTTACACACAGGTACACAC   EV381144M13F TCCCAGTCACGACGTGGGGTGTCTTGCTAATTGCC 1 Koop et al. 2008 EV381144MR GTTTCTGGACTGGCAGAACATGC   EV381440M13F TCCCAGTCACGACGTGTTTGAGCCTAGTGCCAGC 1 Koop et al. 2008 EV381440MR GTTTCTTTGCCAAGAAGGGGCAG   EV381536M13F TCCCAGTCACGACGTTCTGGGACAATAGCGCCAC 1 Koop et al. 2008 EV381536MR GTTTAATGAGCTGCCAACACCTG   EV381733M13F TCCCAGTCACGACGTTTGGCTCTGGTGGACTTGG 1 Koop et al. 2008 EV381733MR GTTTGGTCGACGGTTGTTAGGG   EV381854M13F TCCCAGTCACGACGTGGTAGAGTGGAGCTGGGAC 1 Koop et al. 2008 EV381854MR GTTTACCTGGCCATAGTGAACGG   EV381868M13F TCCCAGTCACGACGTCAGCAGTGTGAAAATTCAGTCC 1 Koop et al. 2008 EV381868MR GTTTGATCCCGTCTTCGTCTCCG   EV381923M13F TCCCAGTCACGACGTCCCCTGTGGTCTAGCTTCC 1 Koop et al. 2008 EV381923MR GTTTGCGTCACGACCTGTAATGG   EV381948M13F TCCCAGTCACGACGTAGAATGGAGGAAAGTGAGGGAG 1 Koop et al. 2008 EV381948MR GTTTCCCATCAGTGGAGTTTGGC   EV381974M13F TCCCAGTCACGACGTACAACAACACATACATACTTGAGG 1 Koop et al. 2008 EV381974MR GTTTCCACCTTGGCCTTGTTAGC   EV382103M13F TCCCAGTCACGACGTGTTAGGCTGCACCATCTGC 1 Koop et al. 2008 EV382103MR GTTTCTGTGTTTAGTCAGGGACAAC   EV382312M13F TCCCAGTCACGACGTACCACTTATCACAATTAACCCAC 1 Koop et al. 2008 EV382312MR GTTTGATTTCTGTGTTGTCTATGGGG         90
Primer name Primer sequence 5'-3' Marker type Reference EV382452M13F TCCCAGTCACGACGTCGAGAGGAGATCCCAGATGC 1 Koop et al. 2008 EV382452MR GTTTGTGGCCTCCCCTGATACTC   EV382642M13F TCCCAGTCACGACGTAAACCAGAGTGGGGAAAGC 1 Koop et al. 2008 EV382642MR GTTTGGGGTGGTAGTATGTGGCG   EV382685M13F TCCCAGTCACGACGTGTTACTGCAGACTCCCAAGC 1 Koop et al. 2008 EV382685MR GTTTATCCACGAACGAGGTCAGG   EV382712M13F TCCCAGTCACGACGTTCAGAGACAGAGGGTCAGC 1 Koop et al. 2008 EV382712MR GTTTCCCCGTGTTTCCCATCATC   EV382726M13F TCCCAGTCACGACGTCAGAGCAGTGAGGGGACTG 1 Koop et al. 2008 EV382726MR GTTTGGAAATCCATTGGCACCCG   EV382831M13F TCCCAGTCACGACGTGAACGACTGACCAGCAGAC 1 Koop et al. 2008 EV382831MR GTTTCCATGACGTCCACAACGAC   EV383063M13F TCCCAGTCACGACGTACAATGAAACCTCAATCGGTC 1 Koop et al. 2008 EV383063MR GTTTCCACTCCTCCTCCTACCC   EV383066M13F TCCCAGTCACGACGTAGTAGCGGAGGGTAAACGC 1 Koop et al. 2008 EV383066MR GTTTGGACGTTTTGTGCCTCCTG   EV383071M13F TCCCAGTCACGACGTACCTCAAGCCCTGAATCCC 1 Koop et al. 2008 EV383071MR GTTTGCACTGTGGAAGCAGACAC   EV383078M13F TCCCAGTCACGACGTATCCTTCTCCAGCCAGCTC 1 Koop et al. 2008 EV383078MR GTTTAAGTACAGCCGTTTTGGCG   EV383188M13F TCCCAGTCACGACGTTCAGTAACCTCCAGGACATCG 1 Koop et al. 2008 EV383188MR GTTTAGCACTGGAACCAGAGACC   EV383294M13F TCCCAGTCACGACGTATGCATTTAGCAGTGGCGG 1 Koop et al. 2008 EV383294MR GTTTCTTCGTCTCTCCCGAGTCC   EV383310M13F TCCCAGTCACGACGTACCACTTATCACAATTAACCCAC 1 Koop et al. 2008 EV383310MR GTTTGATTTCTGTGTTGTCTATGGGG   EV383334M13F TCCCAGTCACGACGTACTGAACCCCAGTACCAGC 1 Koop et al. 2008 EV383334MR GTTTGCCGAGCACAGATAGGAGG   EV383358M13F TCCCAGTCACGACGTGAACGACTGACCAGCAGAC 1 Koop et al. 2008 EV383358MR GTTTCCATGACGTCCACAACGAC   EV383375M13F TCCCAGTCACGACGTGTTCTGGACCTCCAACAGG 1 Koop et al. 2008 EV383375MR GTTTAGCCGTTTGGAACACAACC   EV383489M13F TCCCAGTCACGACGTGCAGGAGATGACCGAGGAG 1 Koop et al. 2008 EV383489MR GTTTGCACAGGGCCTTATCAATGG   EV383512M13F TCCCAGTCACGACGTAGAAGCTGGGTTTGGGCTC 1 Koop et al. 2008 EV383512MR GTTTCGTCCACCGTAAATGCTTC   EV383626M13F TCCCAGTCACGACGTGGCCAAACACTCCAGCAAG 1 Koop et al. 2008 EV383626MR GTTTGCAGACACCACATCACAGC   Ots2F TCCCAGTCACGACGTACACCTCACACTTAGA 2 Banks et al. 1999 Ots2R GTTCTTCAGTGTGAAGGATATTAAA   Ots3F TCCCAGTCACGACGTCACACTCTTTCAGGAG 2 Banks et al. 1999 Ots3R GTTCTTCTTCCATTGTGATTCT or AGAATCACAATGGAAG         91
Primer name Primer sequence 5'-3' Marker type Reference One4F TCCCAGTCACGACGTTAATTTACATATCAGGTTCTGCC 2 Scribner et al. 1996 One4R GTTCTTATGCTAGTCATGGCTCTTACAT   One8F TCCCAGTCACGACGTAACATTCTGGGATGACAGGGGTA 2 Scribner et al. 1996 One8R GTTCTTCTGTTCTGCTCCAGTGAAGTGGA   One12F TCCCAGTCACGACGTACTTATGCTAGTCATGGCTCTT 2 Scribner et al. 1996 One12R GTTCTTCGGTCATCGAAAGATACTTTT   One14F TCCCAGTCACGACGTAGAAACATGAGAACAGTCTAGGT 2 Scribner et al. 1996 One14R GTTCTTCCTTATGAGTTTGGTCTCCATGT   One17F TCCCAGTCACGACGTATGGCAGGATTGTTTTAGGTTGT 2 Scribner et al. 1996 One17R GTTCTTGCCATGAGGAAGACACATCAATA   One102F TCCCAGTCACGACGTCATGGAGAAAAGACCAATCA 2 Olsen et al. 1996 One102R GTTCTTCACTGCCCTACAACAGAAG   One105F TCCCAGTCACGACGTTCTTTAAGAATATGAGCCCTGG 2 Olsen et al. 1996 One105R GTTCTTGCTCAAATAAACTTAAACCTGTCC   One108F TCCCAGTCACGACGTTGCAGAGCCATACTAAACCA 2 Olsen et al. 1996 One108R GTTCTTAAGAATTGAGAGATGCAGGG   One109F TCCCAGTCACGACGTAGGGAGAGAAGAGAGGGAGA 2 Olsen et al. 1996 One109R GTTCTTCCTCAGAAGTAGCATCAGCTC   One110F TCCCAGTCACGACGTCCTCCATTTCAATCTCATCC 2 Olsen et al. 1996 One110R GTTCTTACAGAGAACAGTGAGGGAGC   One111F TCCCAGTCACGACGTATGACCAAGGAGCTTCTGC 2 Olsen et al. 1996 One111R GTTCTTATCCAGGTACTCCACTGGC   One112F TCCCAGTCACGACGTGTGACCCAGACTCAGAGGAC 2 Olsen et al. 1996 One112R GTTCTTCACAACCCATCACATGAAAC   Ssa85M13F TCCCAGTCACGACGTAGGTGGGTCCTCCAAGCTAC 2 O'Reilly et al. 1996 Ssa85MR GTTTACCCGCTCCTCACTTAATC   Omy77M13F TCCCAGTCACGACGTGTTCTCTACTGAGTCAT  2 Morris et al. 1996 Omy77MR GTTTGGGTCTTTAAGGCTTCACTGCA   One2M13F TCCCAGTCACGACGTGGTGCCAAGGTTCAGTTTATGTT 2 Scribner et al. 1996 One2MR GTTTCAGGAATTTACAGGACCCAGGTT   Ots100M13F TCCCAGTCACGACGTTGAACATGAGCTGTGTGAG 2 Nelson et al. 1998, Nelson & Beacham, 1999 Ots100MR GTTTACGGACGTGCCAGTGAG   Ots103M13F TCCCAGTCACGACGTAGGCTCTGGGTCCGTG  2 Beacham et al. 1998, Nelson & Beacham, 1999, Small et al. 1998 Ots103MR GTTTGATATGGTGTGATAGCTGG          92
Table A.2 Additional description of 57 microsatellite loci (EST-linked and putatively neutral) making up the baseline dataset in this study.  Primer name, working name, Genbank accession, microsatellite motif, annealing temperature (Ta), range of allele size and locus behaviour is listed for each locus.    Primer name Working name Genbank accession Microsatellite motif Ta Range of allele size Locus behaviour CA048687M13F CA687 CA048687 (AG)19 60-50 201-203 Neutral CA048687R       CA042613M13F CA613 CA042613 (GT)21 60-50 108-126 Neutral CA042613R       CA039983M13F CA983 CA039983 (GT)13 60-50 273-309 Neutral CA039983R       TAP2BM13F TAP2B Z83326 N/A 60-50 329-341 Neutral TAP2BR       OMM5003M13F OMM5003 CO805109 (GT)3 60-50 196-214 Neutral OMM5003R       OMM5007M13F OMM5007 CO805113 (GT)25 60-50 187-199 Outlier OMM5007R       OMM5008M13F OMM5008 CO805114 (GT)19 60-50 247-267 Neutral OMM5008R       OMM5032M13F OMM5032 CA349143 (CA)13 60-50 189-205 Neutral OMM5032R       OMM5033M13F OMM5033 CA349148 (CA)28 60-50 263-311 Neutral OMM5033R       OMM5037M13F OMM5037 CA348625 ((CA)15 60-50 275-299 Neutral OMM5037R       OMM5053M13F OMM5053 CA349198 (GT)21 60-50 259-317 Neutral OMM5053MR       OMM5058M13F OMM5058 CA348781 (CA)11 48 238-286 Neutral OMM5058MR       OMM5067M13F OMM5067 CA348790 (CA)13 60-50 209-215 Neutral OMM5067MR       OMM5075M13F OMM5075 CA348807 (GT)12 60-50 214-234 Neutral OMM5075MR       OMM5091M13F OMM5091 CA348850 (GA)49(GT)11 60-50 235-249 Outlier OMM5091MR       OMM5099M13F OMM5099 CA348959 (CT)24 60-50 273-297 Neutral OMM5099MR       OMM5108M13F OMM5108 CA349062 (GT)12 60-50 286-302 Neutral OMM5108MR       OMM5121M13F OMM5121 CA349040 (AG)15 58 196-198 Neutral OMM5121MR       OMM5124M13F OMM5124 CA349048 (GT)10 60-50 288-294 Neutral OMM5124MR         93
Primer name Working name Genbank accession Microsatellite motif Ta Range of allele size Locus behaviour OMM5125M13F OMM5125 CO805127 (CA)13 60-50 276-280 Outlier OMM5125MR       EV374740M13F SK740 EV374740 (AT)^8 60-50 266-272 Neutral EV374740MR       EV375170M13F SK170 EV375170 (AC)^6 60-50 216-224 Neutral EV375170MR       EV375597M13F SK597 EV375597 (GT)^8 60-50 184-194 Neutral EV375597MR       EV375911M13F SK911 EV375911 (AG)^7 60-50 242-256 Neutral EV375911MR       EV376475M13F SK475 EV376475 (GT)^12 60-50 225-239  Neutral EV376475MR       EV376862M13F SK862 EV376862 (AC)^6 60-50 222-226 Neutral EV376862MR       EV377149M13F SK149 EV377149 (AT)^12 60-50 204-228 Neutral EV377149MR       EV377249M13F SK249 EV377249 (AC)^7 60-50 216-226 Neutral EV377249MR       EV377291M13F SK291 EV377291 (GT)^8 60-50 253-257 Neutral EV377291MR       EV377365M13F SK365 EV377365 (GT)^8 60-50 226-228 Neutral EV377365MR       EV377769M13F SK769 EV377769 (GT)^11 60-50 171-177 Neutral EV377769MR       EV378220M13F SK220 EV378220 (GT)^11 60-50 199-217 Neutral EV378220MR       Ots06ESFUM13F Ots06 CX351581 (GTAT)n 60-50 174-178 Neutral Ots06ESFUMR       Ots07ESFUM13F Ots07 CX351642 (AT)n 55-45 247-273 Neutral Ots07ESFUMR       Ots14ESFUM13F Ots14 CX352740 (GT)n 60-50 294-312  Neutral Ots14ESFUMR       Ots29ESFUM13F Ots29 EL554574 (CT)n 60-50 254-278 Neutral Ots29ESFUMR       EV379484M13F SK484 EV379484 (AC)9 60-50 169-177 Neutral EV379484MR       EV379691M13F SK691 EV379691 (AC)7 60-50 226-232 Neutral EV379691MR       EV380634M13F SK634 EV380634 (AC)8 60-50 188-192 Neutral EV380634MR       EV380723M13F SK723 EV380723 (AC)9 60-50 233-235 Neutral EV380723MR                94
Primer name Working name Genbank accession Microsatellite motif Ta Range of allele size Locus behaviour EV381536M13F SK536 EV381536 (AT)8 55-45 237-241 Outlier EV381536MR       EV382103M13F SK103 EV382103 (GT)15 60-50 252-268 Neutral EV382103MR       EV382642M13F SK642 EV382642 (GT)10 60-50 248-284 Outlier EV382642MR       EV382685M13F SK685 EV382685 (GT)12 60-50 222-230 Neutral EV382685MR       EV382712M13F SK712 EV382712 (AG)10 60-50 212-214 Neutral EV382712MR       EV383188M13F SK188 EV383188 (AC)24 60-50 231-265 Neutral EV383188MR       EV383358M13F SK358 EV383358 (AC)33 60-50 200- 244 Outlier EV383358MR       EV383626M13F SK626 EV383626 (AC)7 60-50 257-279 Outlier EV383626MR       One8F One8 N/A (CA)24 60-50 214-244 Neutral One8R       One14F One14 N/A (CA)26  55-45 152-172 Neutral One14R       One102F One102 AF274518 (ATCT)10 60-50 228-290 Neutral One102R       One105F One105 AF274521 (TAGA)9 60-50 151-179 Neutral One105R       One108F One108 AF274524 (ATCT)21 60-50 201-293 Neutral One108R       One109F One109 AF274525 (TAGA)9 55-45 150-198  Neutral One109R       One110F One110 AF274526 (TAGA)21 60-50 247-325 Neutral One110R       One112F One112 AF274528 (ATCT)28 60-50 163- 255 Neutral One112R       Ssa85M13F Ssa85 U43692 (GT)14 60-50 135-197 Neutral Ssa85MR                  95
Table A.3 Expanded results of genetic variation at 57 polymorphic microsatellite loci (EST-linked and putatively neutral) in four shore spawning localities (NE-northeast, SE-southeast, NW-northwest, CW-central west), four stream spawning localities (PAC-Peachland Creek, PNC-Penticton Creek, MC-Mission Creek, POC-Powers Creek) and total populations in two ecotypes of Okanagan Lake kokanee.  Sample size (N), number of alleles (Na), observed (Ho) and expected heterozygosity (He) is shown per locus.       Population    Shore ecotype  Stream ecotype Locus     Total NE SE NW CW   Total PAC PNC MC POC OMM5003 N  47 14 13 14 6  46 10 10 17 9  Na  7 4 3 6 3  7 4 4 5 5  Ho  0.574 0.571 0.538 0.714 0.333  0.609 0.700 0.500 0.647 0.556  He  0.514 0.543 0.423 0.602 0.292  0.578 0.570 0.565 0.590 0.519               OMM5007 N  48 14 14 14 6  46 9 10 18 9  Na  6 3 3 3 4  4 3 2 3 2  Ho  0.521 0.500 0.429 0.571 0.667  0.522 0.333 0.800 0.444 0.556  He  0.559 0.541 0.503 0.528 0.583  0.482 0.537 0.480 0.426 0.475               OMM5008 N  45 14 13 12 6  47 10 10 18 9  Na  7 6 5 4 2  7 3 4 5 4  Ho  0.467 0.429 0.538 0.500 0.333  0.574 0.500 0.700 0.611 0.444  He  0.529 0.464 0.648 0.462 0.444  0.529 0.405 0.525 0.568 0.543               OMM5032 N  46 13 14 13 6  47 10 10 18 9  Na  4 4 3 3 3  5 3 3 3 4  Ho  0.696 0.769 0.714 0.538 0.833  0.383 0.300 0.300 0.333 0.667  He  0.651 0.731 0.589 0.618 0.569  0.574 0.505 0.515 0.542 0.623               OMM5033 N  46 14 14 12 6  45 10 9 18 8  Na  10 6 7 7 5  12 8 7 9 8  Ho  0.630 0.571 0.857 0.500 0.500  0.756 0.800 0.667 0.722 0.875  He  0.816 0.773 0.821 0.771 0.667  0.824 0.815 0.790 0.781 0.789               OMM5037 N  46 14 14 12 6  47 10 10 18 9  Na  7 3 5 3 3  4 3 4 3 3  Ho  0.304 0.286 0.357 0.250 0.333  0.255 0.200 0.400 0.222 0.222  He  0.274 0.253 0.316 0.226 0.292  0.267 0.185 0.475 0.204 0.204               OMM5053 N  46 14 13 13 6  47 10 10 18 9  Na  20 13 12 14 9  25 11 9 17 12  Ho  0.935 0.929 0.923 1.000 0.833  0.957 1.000 1.000 0.889 1.000  He  0.923 0.890 0.905 0.914 0.875  0.924 0.890 0.870 0.920 0.901               OMM5058 N  47 14 14 14 5  45 9 9 18 9  Na  14 9 10 12 4  16 8 7 13 8  Ho  0.894 0.929 0.857 0.857 1.000  0.911 0.889 0.889 0.944 0.889  He  0.854 0.804 0.804 0.888 0.640  0.850 0.778 0.778 0.850 0.821                                             96
   Population    Shore ecotype  Stream ecotype Locus     Total NE SE NW CW   Total PAC PNC MC POC OMM5067 N  45 13 12 14 6  46 9 10 18 9  Na  3 3 2 2 3  3 3 3 3 3  Ho  0.267 0.538 0.167 0.071 0.333  0.543 0.556 0.400 0.611 0.556  He  0.354 0.547 0.278 0.069 0.486  0.481 0.438 0.460 0.494 0.494               OMM5075 N  48 14 14 14 6  47 10 10 18 9  Na  5 4 3 3 2  6 3 3 4 3  Ho  0.333 0.429 0.286 0.357 0.167  0.277 0.300 0.200 0.333 0.222  He  0.310 0.411 0.255 0.304 0.153  0.267 0.265 0.185 0.330 0.204               OMM5091 N  48 14 14 14 6  47 10 10 18 9  Na  3 2 1 2 2  4 1 4 3 3  Ho  0.063 0.071 0.000 0.071 0.167  0.234 0.000 0.300 0.333 0.222  He  0.061 0.069 0.000 0.069 0.153  0.247 0.000 0.270 0.356 0.204               OMM5099 N  47 13 14 14 6  46 10 10 18 8  Na  5 3 4 4 2  8 2 3 4 5  Ho  0.553 0.692 0.571 0.571 0.167  0.587 0.400 0.700 0.722 0.375  He  0.568 0.577 0.564 0.564 0.375  0.572 0.480 0.545 0.576 0.656               OMM5108 N  47 14 14 13 6  45 10 10 17 8  Na  6 3 5 4 4  7 5 4 6 3  Ho  0.447 0.214 0.500 0.538 0.667  0.444 0.400 0.400 0.471 0.500  He  0.480 0.196 0.505 0.592 0.597  0.445 0.485 0.345 0.401 0.531               OMM5121 N  47 14 14 13 6  45 9 9 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.681 0.714 0.643 0.769 0.500  0.489 0.222 0.333 0.500 0.889  He  0.489 0.490 0.436 0.497 0.486  0.500 0.494 0.475 0.498 0.494               OMM5124 N  47 14 13 14 6  47 10 10 18 9  Na  3 2 3 2 2  3 2 2 3 2  Ho  0.277 0.286 0.385 0.071 0.500  0.234 0.100 0.200 0.389 0.111  He  0.241 0.245 0.322 0.069 0.375  0.271 0.095 0.320 0.387 0.105               OMM5125 N  48 14 14 14 6  46 10 10 18 8  Na  3 3 3 3 3  3 3 3 3 3  Ho  0.333 0.286 0.214 0.357 0.667  0.587 0.300 0.900 0.500 0.750  He  0.416 0.360 0.357 0.482 0.486  0.582 0.395 0.625 0.512 0.531               CA613 N  48 14 14 14 6  47 10 10 18 9  Na  5 2 2 4 3  4 2 3 2 3  Ho  0.604 0.500 0.643 0.571 0.833  0.511 0.500 0.600 0.444 0.556  He  0.511 0.497 0.436 0.556 0.542  0.510 0.495 0.545 0.444 0.512               CA687 N  46 14 12 14 6  45 8 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.435 0.286 0.417 0.500 0.667  0.400 0.375 0.700 0.278 0.333  He  0.364 0.245 0.413 0.375 0.444  0.391 0.305 0.455 0.375 0.401   97
   Population    Shore ecotype  Stream ecotype Locus     Total NE SE NW CW   Total PAC PNC MC POC CA983 N  46 14 13 13 6  46 9 10 18 9  Na  10 7 9 8 6  15 10 8 14 8  Ho  0.804 0.929 0.769 0.769 0.667  0.891 0.889 0.800 0.944 0.889  He  0.840 0.809 0.852 0.784 0.778  0.871 0.846 0.830 0.884 0.802               SK103 N  46 13 14 14 5  45 9 9 18 9  Na  4 2 3 2 2  5 3 2 4 2  Ho  0.174 0.077 0.286 0.143 0.200  0.133 0.111 0.000 0.167 0.222  He  0.297 0.311 0.357 0.245 0.180  0.338 0.438 0.346 0.252 0.346               SK149 N  48 14 14 14 6  45 9 9 18 9  Na  10 8 8 5 5  12 6 7 8 7  Ho  0.750 0.857 0.857 0.500 0.833  0.778 0.556 0.889 0.722 1.000  He  0.757 0.824 0.783 0.582 0.694  0.799 0.660 0.778 0.836 0.759               SK170 N  48 14 14 14 6  47 10 10 18 9  Na  5 5 4 5 5  5 5 3 3 4  Ho  0.771 0.714 0.786 0.786 0.833  0.681 0.800 0.600 0.778 0.444  He  0.713 0.730 0.704 0.673 0.736  0.590 0.695 0.460 0.606 0.377               SK188 N  48 14 14 14 6  47 10 10 18 9  Na  11 7 8 10 4  13 9 10 9 7  Ho  0.750 0.786 0.643 0.857 0.667  0.809 0.800 0.800 0.778 0.889  He  0.735 0.770 0.643 0.796 0.514  0.789 0.790 0.820 0.748 0.741               SK220 N  48 14 14 14 6  47 10 10 18 9  Na  5 2 4 3 3  6 3 3 4 2  Ho  0.208 0.071 0.357 0.071 0.500  0.234 0.300 0.200 0.278 0.111  He  0.278 0.069 0.365 0.304 0.403  0.216 0.265 0.185 0.252 0.105               SK249 N  48 14 14 14 6  47 10 10 18 9  Na  3 2 2 3 2  3 2 2 2 3  Ho  0.604 0.714 0.500 0.643 0.500  0.532 0.700 0.400 0.389 0.778  He  0.518 0.500 0.477 0.561 0.486  0.534 0.495 0.500 0.486 0.593               SK291 N  48 14 14 14 6  47 10 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.583 0.643 0.643 0.500 0.500  0.383 0.200 0.500 0.444 0.333  He  0.499 0.497 0.497 0.497 0.486  0.482 0.500 0.455 0.444 0.500               SK358 N  48 14 14 14 6  47 10 10 18 9  Na  16 13 11 11 5  13 6 8 11 9  Ho  0.896 0.929 0.857 0.857 1.000  0.830 0.700 0.700 0.944 0.889  He  0.879 0.875 0.837 0.862 0.778  0.848 0.740 0.795 0.863 0.827               SK365 N  48 14 14 14 6  47 10 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.500 0.643 0.357 0.500 0.500  0.489 0.800 0.500 0.389 0.333  He  0.497 0.497 0.497 0.477 0.486  0.492 0.480 0.495 0.486 0.500   98
   Population    Shore ecotype  Stream ecotype Locus     Total NE SE NW CW   Total PAC PNC MC POC SK475 N  48 14 14 14 6  46 10 9 18 9  Na  4 3 3 2 3  3 3 3 3 2  Ho  0.333 0.357 0.357 0.143 0.667  0.326 0.300 0.444 0.333 0.222  He  0.292 0.304 0.309 0.133 0.500  0.332 0.265 0.438 0.364 0.198               SK484 N  48 14 14 14 6  46 10 9 18 9  Na  3 3 2 2 2  4 2 3 3 2  Ho  0.250 0.143 0.286 0.214 0.500  0.217 0.400 0.222 0.167 0.111  He  0.221 0.135 0.245 0.191 0.375  0.198 0.320 0.204 0.156 0.105               SK536 N  47 14 13 14 6  44 9 10 16 9  Na  3 3 3 3 3  3 3 3 3 3  Ho  0.596 0.571 0.692 0.643 0.333  0.568 0.667 0.300 0.625 0.667  He  0.620 0.625 0.607 0.589 0.625  0.589 0.660 0.585 0.477 0.549               SK597 N  47 13 14 14 6  47 10 10 18 9  Na  5 4 2 2 2  2 2 2 2 2  Ho  0.170 0.385 0.071 0.071 0.167  0.191 0.100 0.100 0.167 0.444  He  0.160 0.331 0.069 0.069 0.153  0.173 0.095 0.095 0.153 0.346               SK626 N  48 14 14 14 6  47 10 10 18 9  Na  10 6 7 5 4  6 6 5 5 3  Ho  0.583 0.571 0.857 0.357 0.500  0.489 0.500 0.200 0.500 0.778  He  0.656 0.656 0.704 0.533 0.681  0.504 0.650 0.420 0.417 0.512               SK634 N  48 14 14 14 6  47 10 10 18 9  Na  3 2 3 2 2  3 3 3 3 2  Ho  0.646 0.643 0.643 0.786 0.333  0.574 0.800 0.400 0.556 0.556  He  0.478 0.477 0.472 0.497 0.278  0.559 0.580 0.580 0.554 0.475               SK642 N  48 14 14 14 6  46 9 10 18 9  Na  6 5 4 3 4  4 3 4 3 4  Ho  0.646 0.714 0.571 0.643 0.667  0.587 0.444 0.700 0.500 0.778  He  0.663 0.702 0.630 0.651 0.583  0.684 0.648 0.670 0.660 0.660               SK685 N  48 14 14 14 6  47 10 10 18 9  Na  3 2 2 3 2  4 3 3 4 2  Ho  0.229 0.143 0.286 0.286 0.167  0.234 0.200 0.200 0.278 0.222  He  0.239 0.133 0.337 0.255 0.153  0.298 0.405 0.335 0.250 0.198               SK691 N  48 14 14 14 6  47 10 10 18 9  Na  2 1 2 2 2  2 2 2 2 2  Ho  0.208 0.000 0.143 0.286 0.667  0.213 0.200 0.100 0.278 0.222  He  0.187 0.000 0.133 0.245 0.444  0.190 0.180 0.095 0.239 0.198               SK712 N  48 14 14 14 6  47 10 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.417 0.286 0.357 0.500 0.667  0.404 0.400 0.700 0.278 0.333  He  0.353 0.245 0.375 0.375 0.444  0.390 0.320 0.455 0.375 0.401   99
   Population    Shore ecotype  Stream ecotype Locus     Total NE SE NW CW   Total PAC PNC MC POC SK723 N  48 14 14 14 6  46 9 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.479 0.357 0.500 0.500 0.667  0.478 0.444 0.400 0.556 0.444  He  0.489 0.497 0.436 0.477 0.444  0.496 0.444 0.500 0.475 0.346               SK740 N  48 14 14 14 6  47 10 10 18 9  Na  4 4 3 2 1  3 3 3 3 2  Ho  0.146 0.214 0.143 0.143 0.000  0.213 0.200 0.300 0.167 0.222  He  0.138 0.199 0.135 0.133 0.000  0.196 0.185 0.265 0.156 0.198               SK769 N  47 14 14 13 6  47 10 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.319 0.429 0.286 0.308 0.167  0.277 0.200 0.600 0.222 0.111  He  0.296 0.337 0.245 0.355 0.153  0.268 0.180 0.420 0.278 0.105               SK862 N  48 14 14 14 6  47 10 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.229 0.214 0.214 0.214 0.333  0.383 0.300 0.300 0.444 0.444  He  0.264 0.191 0.293 0.293 0.278  0.359 0.255 0.375 0.401 0.346               SK911 N  48 14 14 14 6  47 10 10 18 9  Na  3 2 3 2 1  2 1 2 2 2  Ho  0.104 0.071 0.143 0.143 0.000  0.085 0.000 0.100 0.111 0.111  He  0.100 0.069 0.135 0.133 0.000  0.081 0.000 0.095 0.105 0.105               Ots06 N  48 14 14 14 6  47 10 10 18 9  Na  2 1 1 2 1  2 2 2 2 2  Ho  0.021 0.000 0.000 0.071 0.000  0.106 0.100 0.100 0.056 0.222  He  0.021 0.000 0.000 0.069 0.000  0.101 0.095 0.095 0.054 0.198               Ots07 N  48 14 14 14 6  46 10 10 18 8  Na  11 7 8 9 6  10 7 8 7 4  Ho  0.771 0.714 0.714 0.857 0.833  0.652 0.600 0.800 0.611 0.625  He  0.728 0.699 0.666 0.740 0.778  0.673 0.705 0.705 0.636 0.602               Ots14 N  48 14 14 14 6  46 10 10 18 8  Na  5 2 2 4 3  4 2 3 2 3  Ho  0.604 0.500 0.643 0.571 0.833  0.522 0.500 0.600 0.444 0.625  He  0.511 0.497 0.436 0.556 0.542  0.513 0.495 0.545 0.444 0.539               Ots29 N  47 13 14 14 6  45 10 10 17 8  Na  5 3 4 4 2  8 2 3 4 5  Ho  0.553 0.692 0.571 0.571 0.167  0.600 0.400 0.700 0.706 0.500  He  0.568 0.577 0.564 0.564 0.375  0.574 0.480 0.545 0.580 0.656               TAP2B N  45 13 14 13 5  46 9 10 18 9  Na  2 2 2 2 2  2 2 2 2 2  Ho  0.578 0.615 0.500 0.538 0.800  0.478 0.556 0.600 0.389 0.444  He  0.498 0.497 0.497 0.488 0.480  0.491 0.500 0.500 0.461 0.494   100
   Population    Shore ecotype  Stream ecotype Locus     Total NE SE NW CW   Total PAC PNC MC POC One102 N  48 14 14 14 6  45 10 10 16 9  Na  13 10 8 12 7  13 8 10 12 10  Ho  0.854 0.786 0.786 1.000 0.833  0.844 0.800 0.800 0.875 0.889  He  0.878 0.824 0.832 0.888 0.819  0.894 0.830 0.870 0.895 0.883               One105 N  48 14 14 14 6  46 9 10 18 9  Na  6 5 5 4 5  8 6 4 4 6  Ho  0.521 0.500 0.500 0.571 0.500  0.696 0.556 0.700 0.722 0.778  He  0.532 0.421 0.508 0.569 0.667  0.658 0.593 0.535 0.665 0.765               One108 N  48 14 14 14 6  45 9 9 18 9  Na  21 12 15 13 7  19 11 9 16 12  Ho  0.896 0.857 0.929 0.929 0.833  0.911 1.000 0.889 0.833 1.000  He  0.923 0.883 0.911 0.888 0.806  0.916 0.889 0.864 0.917 0.901               One109 N  47 14 14 13 6  45 9 9 18 9  Na  12 9 10 8 5  13 9 7 9 10  Ho  0.766 0.857 0.786 0.769 0.500  0.867 0.889 0.889 0.833 0.889  He  0.822 0.837 0.814 0.787 0.667  0.867 0.852 0.802 0.836 0.833               One110 N  48 14 14 14 6  45 9 9 18 9  Na  20 13 11 14 8  18 11 11 15 11  Ho  0.854 0.857 1.000 0.857 0.500  1.000 1.000 1.000 1.000 1.000  He  0.909 0.888 0.867 0.898 0.819  0.917 0.883 0.883 0.906 0.895               One112 N  48 14 14 14 6  45 9 9 18 9  Na  17 9 12 12 6  18 9 10 11 10  Ho  0.854 0.929 0.929 0.857 0.500  0.867 0.778 0.778 0.889 1.000  He  0.883 0.862 0.865 0.872 0.694  0.891 0.809 0.802 0.847 0.870               One14 N  48 14 14 14 6  46 10 9 18 9  Na  10 7 5 6 3  8 4 5 6 4  Ho  0.583 0.643 0.571 0.571 0.500  0.522 0.400 0.556 0.500 0.667  He  0.543 0.628 0.464 0.554 0.403  0.522 0.345 0.525 0.529 0.617               One8 N  48 14 14 14 6  46 9 10 18 9  Na  9 5 6 4 5  9 4 6 7 4  Ho  0.521 0.429 0.429 0.571 0.833  0.565 0.667 0.700 0.444 0.556  He  0.493 0.500 0.372 0.462 0.681  0.588 0.660 0.670 0.465 0.500               Ssa85 N  48 14 14 14 6  47 10 10 18 9  Na  13 8 9 8 7  14 7 11 8 8  Ho  0.708 0.571 0.643 0.857 0.833  0.745 0.800 0.700 0.833 0.556  He  0.826 0.755 0.791 0.829 0.819  0.844 0.750 0.885 0.821 0.784    101
Appendix B Additional data for a genetics-based approach  Table B.1 Second ranking of 57 microsatellite loci (EST-linked and putatively neutral) by their synergistic ability to differentiate between each shore (n=4) and stream (n=4) spawning locality sampled from the Okanagan Lake kokanee population and using the algorithm implemented in BELS (Bromaghin 2008).  The top seven ranked outlier, non-outlier, EST-linked and non-EST-linked microsatellite loci are identified numerically in each subsequent column for use in comparisons of types of markers.  Marker comparison  Selected vs. neutral loci EST-linked vs. non-EST-linked loci Locus name BELS ranking 7 outlier loci Top 7 non-outlier loci Top 7 EST-linked loci Top 7 non-EST-linked loci One112 1  1  1 OMM5053 2  2 1  SK358 3 1  2  OMM5058 4  3 3  One108 5  4  2 One110 6  5  3 One109 7  6  4 OMM5033 8  7 4  One8 9    5 OMM5125 10 2  5  SK149 11   6  One102 12    6 CA983 13   7  Ssa85 14    7 One14 15     SK188 16     OMM5003 17     SK103 18     OMM5007 19 3    SK597 20     OMM5032 21     Ots07 22     SK642 23 4    SK626 24 5    SK249 25     Ots06 26     SK536 27 6    OMM5067 28     CA687 29     SK220 30     OMM5008 31     SK769 32     SK691 33       102
Marker comparison  Selected vs. neutral loci EST-linked vs. non-EST-linked loci Locus name BELS ranking 7 outlier loci Top 7 non-outlier loci Top 7 EST-linked loci Top 7 non-EST-linked loci Ots14 34     OMM5099 35     OMM5108 36     SK740 37     SK634 38     OMM5037 39     Ots29 40     TAP2B 41     SK365 42     SK911 43     SK685 44     SK862 45     OMM5091 46 7    OMM5121 47     SK712 48     OMM5124 49     CA613 50     SK723 51     One105 52     OMM5075 53     SK170 54     SK484 55     SK475 56     SK291 57         103
Appendix C UBC Research ethics board certificate of approval    

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